博碩士論文 104582601 詳細資訊




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姓名 裴孟俊(Manh-Quan Bui)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 擴展矩陣分解用於數據表示
(Extending Matrix Factorization for Data Representation)
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摘要(中) 在本文中,我們提出了幾種新方法 擴展矩陣分解,包括非負矩陣分解(NMF),、複數矩陣分解(CMF)、與主成分分析(PCA)相結合的捲積神經網絡(CNN)。我們的方法不僅僅適用於一般的數據表示,特別的是可用於圖像分析,同時超越了圖像處理領域的最新技術水準。
基於NMF模型的開發,論文設計了兩種約束NMF模型 為了獲得稀疏表示。特別是對於第一個型號我們構建了一個合適的單純錐底座,它結構緊湊且具有很高的泛化能力,我們將此模型命名為魯棒的最大體積約束圖非負矩陣分解(MV_GNMF)。第二,我們添加了新約束 增強稀疏性 代表權。在此,大基錐和稀疏表示 強加於非負矩陣分解 與Kullback-Leibler(KL)分歧(conespaNMF_KL),它通過基礎上的大型單純錐約束和提取特徵上的稀疏正則化來實現稀疏性。
复矩陣分解(CMF) 楷模 是自然延伸 NMF,其中處理複雜數據。這些型號具有廣泛的應用,例如 人臉識別 和臉部表情識別。最近,CMF和示例嵌入複雜矩陣分解(EE-CMF)[37]顯示了面部表情識別中強大的數據表示方法,其中像素密集的實際值轉化為複雜域。按照[37]中的工作,我們開發了CMF方法來增強數據顯示的能力,通過將更多約束集成到EE-CMF模型 中,以獲得圖正則化的示例嵌入复矩陣分解(gEE-CMF)和稀疏性分別用稀疏性約束(sEE-CMF)模型實現樣本嵌入複雜矩陣分解。此外,我們還提出了兩種複雜領域的數據學習方案,即 複雜域(PCMF)和(DPCMF)上的無監督和監督學習方法。
本文的另一個重點貢獻是提出了複雜域上的核方法,我們擴展了用於復雜矩陣分解(DKCMF)的深度核方法,以獲得有效的數據表示。在這項工作中,首先通過由採用的Euler內核定義的顯式映射,將實際數據投影到復雜字段中。然後,建立隱式希爾伯特核空間以將數據投影到高維空間。在特徵空間中,我們應用複雜矩陣因子分解來有效地減少高維數據點的維度,從而獲得新的數據描述符。
主成分分析(PCA)被稱為降維和多變量分析的強大技術,而卷積神經網絡(CNN)是強大的視覺模型,可產生特徵的層次結構。
人臉識別實驗中,人臉表情識別和人體動作識別的實驗表現與比較方法相比,所提出的方法提供更了強大的特徵,並獲得了一致且更好的識別結果。
摘要(英) In this dissertation, we proposed several new approaches to extend matrix factorization including nonnegative matrix factorization (NMF), complex matrix factorization (CMF), and convolution neural networks (CNN) integrating with principal component analysis (PCA). Our approaches are not only specifically suited for data representation in general and for image analyzing in particular but also outperform to the state-of-the-art in image processing field.
Based on the development of NMF models, the thesis designed two constrained NMF models in order to obtain the sparsity representations. Particularly, for the first model, we constructed a proper simplicial cone base which is compact and has high generalization ability. We named this model is the robust maximum volume constrained graph nonnegative matrix factorization (MV_GNMF). For the second one, we added new constraints to enhance the sparseness of representation. In this, a large basis cone and sparse representation were imposed on non-negative matrix factorization with Kullback-Leibler (KL) divergence (conespaNMF_KL). It achieves sparseness from a large simplicial cone constraint on the base and sparse regularize on the extracted features.
Complex matrix factorization (CMF) models are natural extensions of NMFs, in which the complex data is treated. These models have a wide range of applications, e.g. face recognition and facial expression recognition. Recently, CMF and exemplar-embed complex matrix factorization (EE-CMF) [37] show the powerful data representation in facial expression recognition, in which the real value of pixel intensive is transformed into the complex domain. Follow the work in [37], we developed CMF approaches to enhance the ability of data display by integrating more constraints into EE-CMF model such as graph to obtain the graph regularized exemplar-embed complex matrix factorization (gEE-CMF), and sparsity to achieve the exemplar-embed complex matrix factorization with sparsity constraint (sEE-CMF) models, respectively. We also proposed two schemes of data learning on complex field, namely unsupervised and supervised learning methods on the complex domain (PCMF) and (DPCMF).
Principal component analysis (PCA) is known as a powerful technique for dimensionality reduction and multivariate analysis, whereas convolutional neural networks (CNNs) are powerful visual models that yield hierarchies of features. Taking the advances of these models, we proposed the model (CNN-PCA) by combining them together to acquire a discriminative data representation.
Experiments on face recognition, facial expression recognition, and human action recognition reveal that the proposed methods extract robust features and provide consistently better recognition results than compared methods.
關鍵字(中) ★ 數據表示
★ 計算機視覺
★ 非負矩陣分解
★ 複雜矩陣分解
★ 深度學習
★ 卷積神經網絡
★ 特徵提取
關鍵字(英) ★ Data representation
★ Computer vision
★ Non-negative matrix factorization
★ Complex matrix factorization
★ Deep learning
★ Convolution neural network
★ Feature extraction
論文目次 摘要.........I
Abstract....... II
Acknowledge.... IV
List of symbols and abbreviations...... VII
List of Figures........ XI
List of Tables..........XIII
Chapter 1 Introduction......... 1
1.1 Principle component analysis...... 1
1.2 Non-Negative matrix factorization.. 2
1.3 Kernel machine.............. 2
1.4 Complex matrix factorization....... 3
1.5 Convolution neural networks... 3
1.6 Research problem............ 4
1.7 Research objectives and contributions...5
1.8 Thesis overview............. 6
Chapter 2 Preliminaries......... 9
2.1 Matrix theory............... 9
2.2 Optimization in the real domain.... 11
2.3 Optimization in the complex domain...12
2.4 Nonnegative matrix factorization and kernel method ...17
2.5 Deep learning method........ 18
Chapter 3 Extending Nonnegative Matrix Factorization ......22
3.1 Introduction................. 22
3.2 Maximum volume constrained graph nonnegative matrix factorization.................... 23
3.3 Large basic cone and sparse subspace constrained nonnegative matrix factorization with Kullback-Leibler divergence for data representation......31
3.4 Conclusion................... 38
Chapter 4 Extending Matrix Factorization on Complex Domain.......................... 39
4.1 Introduction ............. 40
4.2 Euler mapping and cosine dissimilarity.... 43
4.3 Constrained exemplar-embed complex matrix factorization................... 44
4.4 Projective complex matrix factorization and discriminant projective complex matrix factorization ........................47
4.5 Kernel nonnegative matrix factorization.... 49
4.7 Experiments........ ........55
4.4 Conclusion.............. 70

Chapter 5 Extending Matrix by Jointing Convolution Neural Networks and Principal Component Analysis..... 72
5.1 Introduction......... 72
5.2 The proposed model...........74
5.3 Experiments.........77
5.4 Conclusion...... 80
Chapter 6 Conclusion and Future Works...81
6.1 Conclusion....... 81
6.2 Future Works....... 82
Bibliographies........ 83
Publications List...... 91
參考文獻 [1] L. Elden, Matrix Methods in Data Mining and Pattern Recognition. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, 2007.
[2] N. Halko, P. G. Martinsson, and J. A. Tropp, “Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions,” SIAM Rev., vol. 53, no. 2, pp. 217-288, 2011.
[3] K. Pearson, “On lines and planes of closest fit to systems of points in space,” Phil. Mag, vol. 2, no. 6, pp. 559-572, 1901.
[4] K. Fukunaga, Statistical Pattern Recognition, Acadamic, 1990.
[5] H. Hotelling, “Analysis of a complex of statistical variables into principal components,” JEP, vol. 24, pp. 417-441, 1993.
[6] A. Hyvarinen, J Karhunen, and E. Oja, Independent Component Analysis, Wiley Interscience, 2001.
[7] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711-720, Jul. 1997.
[8] D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization”, Nature, vol. 401, no. 6755, pp. 755-791, 1999.
[9] D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in Proc. NIPS, 2000, pp. 556-562.
[10] P. Hoyer, “Non-negative sparse coding,” in Proc. IEEE Neural Networks for Signal Processing, pp. 557-565, 2002.
[11] P. Hoyer, “Non-negative matrix factorization with sparseness constraints,” J. Mach. Learn., vol. 5, pp. 1457-1469, 2004.
[12] H. Li, T. Adal, W. Wang, D. Emge, and A. Cichocki, “NMF with orthogonality constraints and its application to Raman spectroscopy,” VLSI, vol. 48, pp 83-97, 2007.
[13] N. Guan, D. Tao, Z. Luo, and B. Yuan, “Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent,” IEEE Trans. Image Process., vol 20, no. 7, pp. 2030-2048, 2011.
[14] D. Cai, X. He, X. Wu, and J. Han, “Non-negative matrix factorization on manifold,” in Proc. IEEE Int’l Data Mining (ICDM ’08), pp. 63-72, 2008.
[15] D. Cai, X. F. He, J. W. Han, and T. S. Huang, “Graph regularized non-negative matrix factorization for data representation,” IEEE Trans. Patt. Anal. and Mach. Inte., vol. 33, no. 8, pp. 1548-1560, 2011.
[16] W. S. Zheng, J. H. Lai, S. Liao, and R. He, “Extracting non-negative basis images using pixel dispersion penalty”, Pattern Recognition, vol. 45, no. 8, pp. 2912–2926, 2012.
[17] W. Xu, X.Liu, and Y. Gong, “Document clustering based on non-negative matrix factorization,” in Proc. Int. ACM Conf. on Research and development in information retrieval (SIGIR), pp. 267-273, 2003.
[18] V. P. Pauca, F. Shahnaz, M. W.Berry, and R. J. Plemmons, “Text mining using non-negative matrix factorizations,” in SDM ’04: Proc. of SIAM Int. Conf. on Data Mining, pp. 452-456, 2004.
F. Shahnaz, M. W. Berry, V. P. Pauca, and R. J. Plemmons, “Document clustering using nonnegative matrix factorization,” Inf. Process. Manage. vol. 42, pp. 373-386, 2006.
[20] A. Cichocki and A. H. Phan, “Fast local algorithms for large scale nonnegative matrix and tensor factorizations,” IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, vol. E92A, no. 3, pp. 708-721, 2009.
[21] C. Fevotte and J. Idier, “Algorithms for nonnegative matrix factorization with the beta-divergence,” Neural Comput., vol. 23, no. 9, pp. 2421-2456, 2011.
[22] C. Fevotte, N. Bertin, and J. L. Durrieu, “Nonnegative matrix factorization with the Itakura-Saito divergence: with application to music analysis,” Neural Comput., vol. 21, no. 3, pp. 793-830, 2009.
[23] Manh-Quan Bui, Viet-Hang Duong, Seksan Mathulaprangsan, Bach-Tung Pham, Justin Lee, Jia-Jing Wang, “A Survey of Polyphonic Sound Event Detection Based on Non-negative Matrix Factorization”, in Proc. International Computer Syposium, Dec. Taiwan 2016.
[24] A. Cichocki, R. Zdunek, and S. Amari, “Csisz´ar’s divergences for non-negative matrix factorization: Family of new algorithms,” In Int. Conf. Independent Component Analysis and Signal Separation, pp. 32-39, 2006.
[25] D. Kong, C. Ding, and H. Huang, “Robust nonnegative matrix factorization using L2,1 norm,” in Proc. ACM Int. Conf. Information and Knowledge Management, pp. 673-682, 2011.
[26] R. Sandler and M. Lindenbaum, “Nonnegative matrix factorization with Earth Mover’s distance metric for image analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1590-1602, 2011.
[27] N. Guan, D. Tao, Z. Luo, and J. Shawe-Taylor, “MahNMF: Manhattan non-negative matrix factorization,” 2012, [Online]. Available: http://arxiv.org/abs/1207.3438.
[28] A. Cichocki, S. Cruces, and S. Amari, “Generalized alpha-beta divergences and their application to robust nonnegative matrix factorization,” Entropy, vol. 13, no. 1, pp. 134-170, 2011.
[29] N. Guan, D. Tao, Z. Luo, and J. Shawe-Taylor, “MahNMF: Manhattan non-negative matrix factorization,” 2012, [Online]. Available: http://arxiv.org/abs/1207.3438.
[30] B. Schölkopf, A. J. Smola, and K.-R. Müller, “Nonlinear component analysis as a kernel eigenvalue problem,” Neural Comput., vol. 10, no. 5, pp. 1299-1319, 1998.
[31] S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.-R. Müller, “Fisher discriminant analysis with kernels,” in Proc. IEEE Int. Workshop Neural Netw. Signal Process, Aug. 1999, pp. 41-48
[32] I. Buciu, N. Nikolaidis, and I. Pitas, “Non-negative matrix factorization in polynomial feature space,” IEEE Trans. Neural Netw., vol. 19, pp. 1090-1100, 2007
[33] S. Zafeiriou and M. Petrou, “Nonlinear non-negative component analysis algorithms,” IEEE Trans. Image Process., vol. 19, no. 4, pp.1050-1066, 2009.
[34] S. Nikitidis, A. Tefas, and I. Pitas, “Projected gradients for subclass discriminant nonnegative subspace learning,” IEEE Trans. Cybern., vol. 44, no. 12, pp. 2806-2819, Dec. 2014.
[35] B. Scholkopf and A. Smola, Learning with Kernels, Cambridge, MA: MIT Press, 2002.
[36] V. H. Duong, Y. S. Lee, B. T. Pham, S. Mathulaprangsan, P. T. Bao, and J. C. Wang, 2016, “Complex matrix factorization for face Rrecognition,”
[Online] Available: https://arxiv.org/ftp/arxiv/papers/1612/1612.02513.pdf
[37] V. H. Duong, Y. S. Lee, J. J. Ding, B. T. Pham, M. Q. Bui, P. T. Bao, and J. C. Wang, “Exemplar-embed complex matrix factorization for facial expression recognition,” in Proc. the 42nd Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp.
[38] V. H. Duong, M. Q. Bui, J. J. Ding, Y. S. Lee, B. T. Pham, P. T. Bao, and J. C. Wang, “A new approach of matrix factorization on complex domain for data representation,” IEICE Trans. on Information and Systems, vol. 100, no. 12, p. 3059-3063, 2017.
[39] Y. LeCun, F. J. Huang, and L. Bottou, “Learning methods for generic object recognition with invariance to pose and lighting,” in Proc IEEE Comp. visi. part. regc.(CVPR), 2004, pp. 96-104.
[40] Y. LeCun, K. Kavukcuoglu, and C. Farabet, “Convolutional networks and applications in vision,” in IEEE International Symposium on Circuits and Systems (ISCAS), 2010, pages 253-256.
[41] C. Colombo, D. Comanducci, and A. Del Bimbo, “Compact representation and probabilistic classification of human actions in videos,” in Proc. IEEE AVSS, 2007, pp. 342-346.
[42] Q. Le, W. Zou, S. Yeung, and A. Ng, “Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis,” in Proc. IEEE CVPR, 2011, pp. 3361-3368.
[43] S. Ji, W. Xu, M. Yang, and K. Yu, “3D convolutional neural networks for human action recognition,” IEEE Trans. PAMI, vol. 35, no.1, pp. 221-231, 2013.
[44] D. Tran, L. Bthedev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proc. IEEE ICCV, 2015, pp. 4489-4497.
[45] R. A. Horn and C. R. Johnson, Topics in matrix analysis, Cambridge University Press. VIII, pp. 607 , 1991.
[46] S. Boyd and L. Vandenberghe, Convex optimization, Cambridge University Press, 2004.
[47] J. M. Borwein and A. S. Lewis, Convex analysis and nonlinear optimization: Theory and Examples, Springer-Verlag, 2006.
[48] Erwin Kreyszig, Advanced engineering mathematics, International Student Version, John Wiley and Sons, 2011.
[49] B. P. Palka, An introduction to complex function theory, Springer, 1991.
[50] M. J. Ablowitz and A. S. Fokas, Complex variables, Cambridge, 2003.
[51] M. Faijul Amin. “Wirtinger calculus based gradient descent and Levenberg-Marquardt learning algorithms in complex-valued neural networks,” Lecture Notes in Computer Science, 2011
[52] A. Hjorunges and D. Gesbert, “Complex-valued matrix differentiation: Techniques and Key Results," IEEE Trans. Signal Process., vol. 55, no. 6, pp. 2740-2746, 2007.
[53] J. R. Magnus and H. Neudecker, Matrix differntail calculus with application in statistics and econometrics, Essex, England: John Wiley & Sons, Inc., 1988.
[54] V. H. Duong, W. Hsieh, P. T. Bao, and J. C. Wang, “An overview of kernel based nonnegative matrix factorization,” in Proc IEEE International Conference on Orange Technologies (ICOT), 2014, pp. 227-231.
[55] B. Scholkopf and A. J. Smola, Learning with kernels: Support vector machines, regularization, optimization, and beyond, MIT Press, Cambridge, MA, USA, 2001.
[56] A. Krizhevsky, I. Sutskever, and G.E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. NIPS, 2012.
[57] R. Zhi, M. Flierl, Q. Ruan, and W. B. Kleijn, “Graph preserving sparse nonnegative matrix factorization with application to facial expression recognition,” IEEE Trans. Syst. Man Cybern., Part B, Cybern., vol. 41, no. 1, pp. 38-52, 2011.
[58] X. W. Chen and T. Huang, “Facial expression recognition: a clustering-based approach,” Pattern Recogn. Lett., vol. 24, no. 9, pp. 1295-1302, 2003.
[59] S. Nikitidis, A. Tefas, N. Nikolaidis, and I. Pitas, “Subclass discriminant nonnegative matrix factorization for facial image analysis,” Pattern Recogn., vol. 45, no. 12, pp. 4080-4091, 2012.
[60] G. Zhou, S. Xie, Z. Yang, J. M. Yang, and Z. He, “Minimum volume constrained nonnegative matrix factorization: enhanced ability of learning parts,” IEEE Trans. Neural Netw., vol. 22, no. 10, pp. 1626-1637, Oct. 2011.
[61] T. Liu, M. Gong, and D. Tao, “Large-cone nonnegative matrix factorization,” IEEE Trans. Neural Netw. Learn. Syst., Jun. 2016, doi: 10.1109/TNNLS.2016.2574748.
[62] D. Cai, X. F. He, J. W. Han, and T. S. Huang, “Graph regularized non-negative matrix factorization for data representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1548-1560, 2011.
[63] J. Yang, S. Yang, Y. Fu, X. Li, and T. Huang, “Nonnegative graph embedding,” IEEE CVPR, 2008, pp. 1-8.
[64] H. Zhang, Z. J. Zha, Y. Yang, S. Yan, and T. S. Chua, “Robust (semi) nonnegative graph embedding. IEEE Trans. Image Process.,” vol. 23, no. 1, pp. 2996-3012, 2014.
[65] G. Strang, Linear Algebra and Its Applications, 4th ed., Thomson, Brooks/Cole, Belmont, Ca, 2006.
[66] F. Nie, D. Xu, I. W. H. Tsang, and C. Zhang, “Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction,” IEEE Trans. Image Processing, vol. 19, pp. 1921-1932, 2010.
[67] S. Sun, Z. Hussain, and J. Shawe-Taylor, “Manifold-preserving graph reduction for sparse semi-supervised learning,” Neurocomputing, vol. 124, pp. 13-21, 2014.
[68] I. Buciu and I. Pitas, “A new sparse image representation algorithm applied to facial expression recognition,” in Proc. MLSP, pp. 539-548, 2004.
[69] C. Lin, “Projected gradient methods for non-negative matrix factorization,” Neural Comput., vol. 19, pp. 2756-2779, 2007.
[70] P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended Cohn–Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Workshops, 2010, pp. 94-101.
[71] M. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, “Coding facial expressions with Gabor wavelets,” in Proc. 3rd IEEE Int. Conf. Automatic Face and Gesture Recognition, 1998, pp. 200-205.
[72] H. Kim, and H. Park, “Sparse non-negative matrix factorization via alternating non-negativity constrained least squares for microarray data analysis,” Bioinformatics, vol. 23, no. 12, pp. 1495-1502, 2007.
[73] Y. X. Wang and Y. J. Zhang, “Nonnegative matrix factorization: a comprehensive review,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 6, pp. 1336-1354, 2013.
[74] B. A. Olshausen, and D. F. Field, “Sparse coding with an over-complete basis set: A strategy employed by VI,” Vis. Res., vol. 37, pp. 3311-3325, 1997.
[75] 10. V. H. Duong, Y. S. Lee, B. T. Pham, S.Mathulaprangsan, P. T. Bao, J. J. Wang, “Spatial Dispersion Constrained NMF for Monaural Sthece Separation”, in Proc. the 10th International Symposium on Chinese Spoken Language Processing, China 2016.
[76] J. Eggert, and E. Korner, “Sparse Coding and NMF,” in Proc. IEEE Int. Conf. Neural Networks, 2004, pp. 2529-2533.
[77] T. Virtanen, “Monaural Sound Sthece Separation by Non-Negative Factorization with Temporal Continuity and Sparseness Criteria,” IEEE Trans. on Audio, Speech, and Language Processing, vol. 15(3), pp. 1066-1074, 2007.
[78] A. P. Montano, J.M. Carazo, K. Kochi, D. Lehmann, and R. D. Pascual-Marqui, “Nonsmooth Non-Negative Matrix Factorization (nsNMF)”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28(3), pp. 403-415, 2006.
[79] S. Li, X. Hou, H. Zhang, and Q. Cheng, “Learning Spatially Localized, Parts-Based Representation,” in Proc. IEEE CVPR, 2001, pp. 1-6.
[80] P. Smaragdis, “Nonnegative Matrix Factor Deconvolution; Extraction of Multiple Sound Stheces from Monophonic Inputs,” International Symposium on ICA and BSS, 2004.
[81] S. Zafeiriou, A. Tefas, I. Buciu, I. Pitas, “Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification,” IEEE Transactions on Neural Networks, vol. 17, pp. 683-695, 2006.
[82] D. Cai, X. Wang, and X. He, “Probabilistic dyadic data analysis with local and global consistency,” in Proc. the 26th Annual International Conference on Machine Learning (ICML’09), ACM, 2009, New York, NY, pp. 105-112.
[83] Y. X. Wang and Y. J. Zhang, “Nonnegative matrix factorization: a comprehensive review,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 6, pp. 1336-1354, Jun. 2013.
[84] C. Ding, T. Li, and M. I. Jordan, “Convex and semi-nonnegative matrix factorizations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 45-55, Jan. 2010.
[85] D. Wang, T. Li, and C. Ding, “Weighted feature subset nonnegative matrix factorization and its applications to document understanding,” in Proc. the 10th IEEE International Conference on Data Mining (ICDM), 2010, pp. 541-550.
[86] N. Guan, D. Tao, Z. Luo, and B. Yuan, “NeNMF: an optimal gradient method for non-negative matrix factorization,” IEEE Transactions on Signal Processing, vol. 60, no. 6, pp. 2882-2898, Jun. 2012.
[87] J. Nocedal and S. J. Wright, Numerical Optimization, Springer-Verlag, 2000.
[88] Z. Yuan and E. Oja, “Projective nonnegative matrix factorization for image compression and feature extraction,” in Scandinavian Conf. Image Analysis, 2005, pp. 333-342.
[89] N. Guan, X. Zhang, Z. Luo, D. Tao, and X. Yang, “Discriminant projective non-negative matrix factorization,” PLoS ONE, vol. 8, no.12, 28-3291, 2013.
[90] C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining Knowledge Discov., vol. 2, no. 2, pp. 121-167, 1998.
[91] A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statist. Comput., vol. 14, no. 3, pp. 199-222, 2004.
[92] N. Aronszajn, “Theory of reproducing kernels,” Trans. Amer. Math.Soc., vol. 68, no. 3, pp. 337-404, 1950.
[93] S. Saitoh, Theory of reproducing kernels and its applications, Harlow, U.K.: Longman Scientific & Technical, 1988.
[94] D. Zhang, Z. H. Zhou, and S. Chen, “Nonnegative matrix factorization on kernels,” Trends in Artificial Intelligence, pp. 404-412, Aug. 2006.
[95] D. Zhang and W. Liu, “An efficient nonnegative matrix factorization approach in flexible kernel space,” in Proc. Int. Conf. on Artificial Intelligence, Jul. 2009, pp. 1345-1350.
[96] S. Liwicki, G. Tzimiropoulos, S Zafeiriou, and M. Pantic, “Euler principal component analysis,” Int. J. Comput. Vis., vol. 1, pp. 498-518, 2013.
[97] H. Lee, A. Cichocki, and S. Choi, “Kernel nonnegative matrix factorization for spectral EEG feature extraction,” Neurocomput, vol. 72, no. 13-15, pp. 3182-3190, 2009.
[98] Y. Li and A. Ngom, “A new kernel nonnegative matrix factorization and its application in microarray data analysis,” in IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), San Diego, CA, USA, May 9-12 2012, pp. 371-378.
[99] Q. Yu, R. Wang, B. N. Li, X. Yang, and M. Yao, “Robust locality preserving projections with Cosine-based dissimilarity for linear dimensionality reduction,” IEEE Access, vol. 5, pp. 2676-2684, 2016.
[100] The ORL database of Face. Website,
http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
[101] Database by Georgia Institute of Technology. Website,
http://www.anefian.com/research/facereco.html.
[102] A. Georghiades, “Yale face database,” Center for computational Vision and Control at Yale University. Website,
http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html (2002).
[103] Q. Wang, Kernel principal component analysis and its applications in face recognition and active shape models, 2012. [Online] Available: https://arxiv.org/pdf/1207.3538.pdf
[104] M. H. Van Benthem and M. R. Keenan, “Fast algorithm for the solution of large-scale non-negaive constrained least squares problems,” Jthenal of Chemometrics, vol. 18, pp. 441-450, 2004.
[105] J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, and R. Moore, “Real-time human pose recognition in parts from single depth images,” ACM on Communication, vol. 56, no. 1, pp. 116-124, Jan. 2013.
[106] X. Yang, C. Zhang, and Y. Tian, “Recognizing actions using depth motion maps-based histograms of oriented gradients,” in Proc. ACM ICM, Nov. 2012, pp. 1057-1060.
[107] O. Oreifej and Z. Liu, “HON4D: Histogram of oriented 4dnormals for activity recognition from depth sequences,” in Proc. IEEE CVPR, 2013, pp. 716-723.
[108] H. Rahmani, D. Q. Huynh, A. Mahmood, and A. Mian, “Discriminative human action classification using locality constrained linear coding,” Pattern Recognition Letters, Elsevier, vol. 72, pp. 62-71, Mar. 2016.
[109] H. Rahmani, A. Mahmood, A. Mian, and D. Huynh, “Real time action recognition using histograms of depth gradients and random decision forests,” in Proc. IEEE WACV, 2014.
[110] A. Shahroudy, T.T. Ng, Q. Yang, and G. Wang, “Multimodal multipart learning for action recognition in depth videos,” IEEE PAMI, vol. 38, no. 10, pp. 2123-2129, 2016.
[111] X. Yang and Y. Tian, “Super normal vector for activity recognition using depth sequences,” in Proc. IEEE CVPR, 2014, pp. 804-811.
[112] I. N. Junejo, E. Dexter, I. Laptev, and P. Perez, “Viewindependent action recognition from temporal self-similarities,” IEEE PAMI, vol. 33, no. 1, pp. 172–185, Jan. 2011.
[113] A. Gupta, J. Martinez, J. J. Little, and R. J. Woodham, “3D pose from motion for cross-view action recognition via nonlinear circulant temporal encoding,” in Proc. IEEE CVPR, 2014, pp. 2601-2608.
[114] R. Vemulapalli, F. Arrate, and R. Chellappa, “Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group,” in Proc. IEE CVPR, 2014, pp. 588-595.
[115] J. Wang, Z. Liu, and Y. Wu, Learning Actionlet Ensemble for 3D Human Action Recognition, Springer, chapter 2, pp. 11-40, Jan. 2014.
[116] H. Rahmani, A. Mahmood, D. Q Huynh, and A. Mian, “HOPC: Histogram of oriented principal components of 3D pointclouds for action recognition,” in Proc. ECCV, 2014, pp. 742-757.
[117] H. Rahmani and A. Mian, “Learning a non-linear knowledge transfer model for cross-view action recognition,” in Proc. IEEE CVPR, 2015, pp. 2458-2466.
[118] H. Rahmani, A. Mahmood, D. Q Huynh, and A. Mian, “Histogram of oriented principal components for cross-view action recognition,” IEEE Trans. PAMI, vol. 38, no. 12, pp. 2430-2443, 2016.
[119] H. Rahmani, A. Mian, and M. Shah, “Learning a deep model for human action recognition from novel viewpoints,” IEEE Trans. PAMI, vol. PP, no. 99, pp. 1-1, 2016.
[120] H. Rahmani and A. Mian, “3D action recognition from novel viewpoints,” in Proc. IEEE CVPR, 2016, pp. 1506-1515.
[121] J. Wang, X. Nie, Y. Xia, Y. Wu, and S. Zhu, “Cross-view action modeling, learning and recognition” in Proc. IEEE CVPR, 2014, pp. 2649-2656.
[122] W. Li, Z. Zhang, and Z. Liu, “Expandable data-driven graphical modeling of human actions based on salient postures,” IEEE Trans. CSVT, vol. 18, no. 11, pp. 499–1510, 2008.
[123] X. Yang, C. Zhang, Y.L. Tian, “Recognizing actions using depth motion maps-based histograms of oriented gradients,” in Proc. ACM ICM, 2012, pp. 1057-1060.
[124] J. Wang, Z. Liu, J. Chorowski, Z. Chen, Y. Wu, “Robust 3D action recognition with random occupancy patterns,” in Proc. ECCV, 2012, pp. 872-885.
[125] L. Xia and J. Aggarwal, “Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera,” in Proc. IEEE CVPR, 2013, pp. 2834-2841.
[126] J. Wang, Z. Liu, Y. Wu, J. Yuan, “Mining actionlet ensemble for action recognition with depth cameras,” in Proc. IEEE CVPR, 2012.
[127] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proc. IEEE CVPR, 2014, pp. 1725-1732.
[128] A. Krizhevsky, I. Sutskever, and G.E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. NIPS, 2012.
[129] L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. “Dimensionality reduction: a comparative review,” Tilburg University Technical Report, TiCC-TR 2009-005, 2009, pp. 1-3.
[130] Z. Cheng, L. Qin, Y. Ye, Q. Huang, and Q. Tian, “Human daily action analysis with multi-view and color-depth data,” in Proc. ECCVW, Springer, 2012, pp. 52-61.
[131] R. Li and T. Zickler, “Discriminative virtual views for crossview action recognition,” in Proc. IEEE CVPR, Jun. 2012.
[132] Z. Zhang, C. Wang, B. Xiao, W. Zhou, S. Liu, and C. Shi, “Cross-view action recognition via a continuous virtual path,” in Proc. IEEE CVPR, 2013, pp. 2690-2697.
[133] A. Vedaldi and K. Lenc, “MatConvNet-Convolutional Neural Networks for MATLAB,” in Proc. ACM ICM, 2015.
[134] B. Bader and T.G. Kolda, “Tensor Toolbox Version 2.5,” [Online]. Available:
http://www.sandia.gov/~tgkolda/TensorToolbox/.
[135] R. E. Fan, K. W. Chang, C. J. Hsieh, X.R. Wang, and C. J. Lin, “LIBLINEAR: a library for large linear classification,” Journal of Machine Learning Research, pp. 1871-1874, Aug. 2008.
指導教授 王家慶(Jia-Ching Wang) 審核日期 2019-5-1
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