聯合具有約束非負矩陣分解的支持向量機及其應用

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：96

、訪客IP：3.15.26.116

姓名

林梅(Mai Lam) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

聯合具有約束非負矩陣分解的支持向量機及其應用
(Joint Support Vector Machine with Constrained Nonnegative Matrix Factorization and Its Applications)

相關論文

★ Single and Multi-Label Environmental Sound Recognition with Gaussian Process	★ 波束形成與音訊前處理之嵌入式系統實現
★ 語音合成及語者轉換之應用與設計	★ 基於語意之輿情分析系統
★ 高品質口述系統之設計與應用	★ 深度學習及加速強健特徵之CT影像跟骨骨折辨識及偵測
★ 基於風格向量空間之個性化協同過濾服裝推薦系統	★ RetinaNet應用於人臉偵測
★ 金融商品走勢預測	★ 整合深度學習方法預測年齡以及衰老基因之研究
★ 漢語之端到端語音合成研究	★ 基於 ARM 架構上的 ORB-SLAM2 的應用與改進
★ 基於深度學習之指數股票型基金趨勢預測	★ 探討財經新聞與金融趨勢的相關性
★ 基於卷積神經網路的情緒語音分析	★ 運用深度學習方法預測阿茲海默症惡化與腦中風手術存活

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本研究的目的是研究最大邊際分類限制對於約束非負矩陣分解目標函數的影響。
非負矩陣分解（NMF）是基於特徵空間的降維技術。不幸的是，大多數現有NMF的方法並不足以編碼高階數據信息，且忽略了數據集中的局部幾何結構。此外，在以往的方法中分類步驟和矩陣分解步驟為獨立分開進行。第一個執行數據轉換，第二個利用支持向量機（SVM）分類那些轉換後的數據。
因此，在這項研究中，我們使用統一最大化邊際分類限制於限制型NMF的最佳化以結合SVM與限制型NMF。所提出的演算法是從NMF算法通過利用空間屬性和保護圖型結構屬性所推導出來的。還提出了一種乘法演算法來更新，並解決對應的最佳化問題。
基準圖像數據集的實驗結果證明了該方法的有效性。結果說明，我們提出的算法提供了更好的臉部表示，並且比標準非負矩陣分解及其變體獲得更高的識別率。

摘要(英)

The purpose of this study is to investigate the effects of merging maximum margin classification constraints on the constrained non-negative matrix factorization objective function.
Non-negative matrix factorization (NMF) is a dimension-reduction technique based on a low-rank approximation of the feature space. Unfortunately, most existing NMF based methods are not ready for encoding higher-order data information and ignore the local geometric structure contained in the data set. Furthermore, the previous classification approaches which the classification and matrix factorization steps are separated independently. The first one performs data transformation and the second one classifies the transformed data using classification methods as support vector machine (SVM).
In this research, therefore, we joint SVM and constrained NMF into one by uniting maximum margin classification constraints into the constrained NMF optimization. The proposed algorithm is derived from NMF algorithm by exploiting both spatial and graph-preserving properties. A multiplicative updating algorithm is also proposed to solve the corresponding optimization problem.
Experimental results on benchmark image data sets demonstrate the effectiveness of the proposed method. The results show that our proposed algorithm provides better facial representations and achieves higher recognition rates than standard non-negative matrix factorization and its variants.

關鍵字(中)

★ 非負矩陣分解
★ 支持向量機
★ 空間約束
★ 圖形正則化
★ 面部識別

關鍵字(英)

★ nonnegative matrix factorization
★ support vector machine
★ spatial constrain
★ graph regularization
★ face recognition

論文目次

Abstract ii
Acknowledgement iii
Contents iv
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Research Objective 2
1.3 Research Methodology 3
1.4 Contribution 4
1.5 Outline of Thesis 5
Chapter 2 Related Works 6
2.1 Non-negative Matrix Factorization 6
2.2 Non-negative Matrix Factorization and Variants 8
2.2.1 Spatial NMF 8
2.2.2 Graph NMF 10
2.2.3 Max-margin Semi-NMF 12
2.3 Support Vector machine 14
2.3.1 SVM for Linearly Separable Data 15
2.3.2 SVM for Non-linearly Separable Data 16
2.3.3 Types of kernel 17
2.3.4 Advantages and Disadvantages of SVM 18
Chapter 3 Proposed Method 19
3.1 Introduction 19
3.2 Objective Function 19
3.3 Update the Projection Vector and Slack Variables 20
3.4 Update the Coefficient Matrix 20
3.5 Update the Basis Matrix 21
3.6 Classification 23
Chapter 4 MNMFSGR for Face Recognition 24
4.1 Face Recognition 24
4.1.1 Problem Definition 24
4.1.2 Approaches 25
4.1.3 Structure and Procedure 26
4.1.4 Applications 26
4.2 Experiments 27
4.2.1 Data Preparation 28
4.2.2 Experimental Design 29
4.2.3 Experimental Setup 30
4.2.4 Experimental Results 30
Chapter 5 Other Applications 36
5.1 Facial Expression Recognition 36
5.1.1 Introduction 36
5.1.2 Approaches 38
5.1.3 Experimental Results 41
5.2 Action Recognition 45
5.2.1 Introduction 45
5.2.2 Histogram of Oriented Gradients (HoG) 45
5.2.3 Experimental Results 46
Chapter 6 Conclusion 48
List of References 49

參考文獻

[1] W. Zhao and R. Chellappa, “Face recognition: A literature survey,” ACM Comput. Surv. Comput. Surv., vol. 35, no. 4, pp. 399–458, 2003.
[2] L. Wiskott and J.-M. Fellous, “Face recognition by elastic bunch graph matching,” Pattern Anal. …, vol. 19, no. 7, pp. 775–779, Jul. 1997.
[3] L. Wiskott and C. von der Malsburg, “Recognizing faces by dynamic link matching.,” Neuroimage, vol. 4, no. 3 Pt 2, pp. S14–8, Dec. 1996.
[4] H. Moon and P. J. Phillips, “Computational and performance aspects of PCAbased face-recognition algorithms.,” Perception, vol. 30, no. 3. pp. 303–21, Jan- 2001.
[5] L.-F. Chen, H.-Y. M. Liao, M.-T. Ko, J.-C. Lin, and G.-J. Yu, “A new LDA-based face recognition system which can solve the small sample size problem,” Pattern Recognit., vol. 33, no. 10, pp. 1713–1726, Oct. 2000.
[6] M. Bartlett, “Face recognition by independent component analysis,” Neural Networks, IEEE …, vol. 13, no. 6, pp. 1450–1464, 2002.
[7] H. Wechsler, “Enhanced Fisher linear discriminant models for face recognition,” Proceedings. Fourteenth Int. Conf. Pattern Recognit. (Cat. No.98EX170), vol. 2, pp. 1368–1372.
[8] D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization”, Advances in Neural Information Processing System, 2000.
[9] V. Pavlovic and D. N. Metaxas, “A hybrid face recognition method using Markov random fields,” Proc. 17th Int. Conf. Pattern Recognition, 2004. ICPR 2004., no. 3, pp. 157–160 Vol.3, 2004.
[10] J. Wang, I. Almasri, and X. Gao, “Adaptive graph regularized nonnegative matrix factorization via feature selection,” in 21st International Conference on Pattern Recognition (ICPR 2012), 2012, no. Icpr, pp. 963–966.
[11] W. Zhao, R. Chellappa, P. J. Phillips and A. Rosenfeld, "Face Recognition: A Literature Survey," ACM Computing Surveys, Vol. 35, No. 4, pp. 399–458 , December 2003.
[12] B. Fasel, J. Luettin, “Automatic facial expression analysis: a survey”, Pattern Recognition, 36 (1) (2003), pp. 259-275.
[13] Ekman, P.; Friesen, W.V, "Constants across cultures in the face and emotion”, Journal of Personality and Social Psychology, 17: 124–129, 1971.
[14] Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010), “The Extended Cohn-Kande Dataset (CK+): A complete facial expression dataset for action unit and emotion-specified expression”, Third IEEE Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), 2010.
[15] Aggarwal, J., Ryoo, “M.: Human activity analysis: A review”, ACM Computing Surveys 43(3), 16:1–16:43 (Apr 2011).
[16] Poppe, R., “A survey on vision-based human action recognition”, Image and Vision Computing 28(6), 976 – 990 (2010).
[17] F. Baumann, “Action recognition with hog-of features”, in GCPR, ser. Lecture Notes in Computer Science, J. Weickert, M. Hein, and B. Schiele, Eds. , vol. 8142. Springer, 2013, pp. 243-248.
[18] N. Dalal, B. Triggs, "Histograms of oriented gradients for human detection", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1-8, Jun. 2005.
[19] Schuldt, C., Laptev, I., Caputo, B., “Recognizing human actions: a local svm approach”, In: Pattern Recognition. (ICPR), Proceedings of the 17th International Conference on. vol. 3, pp. 32–36 Vol.3 (2004).
[20] D. D. Lee and H.S. Seung. “Learning the parts of objects by nonnegative matrix factorization”. Nature, 401(6755), pp.788–791, 1999.
[21] W. S. Zheng, J. Lai, S. Liao, R. He, “Extracting non-negative basis images using pixel dispersion penalty”, Pattern Recognition, pp. 2912-2926, 2012.
[22] D. Cai, X. He, J. Han and T. Huang, “Graph regularized nonnegative matrix factorization for data representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33(8), pp. 1548–1560, 2011.
[23] B. G. Kumar, I. Kotsia and I. Patras, “Max-margin nonnegative matrix factorization”, Image Vision Computing, pp. 279–291, 2012.
[24] D. Liu and X. Tan, “Max-margin non-negative matrix factorization with flexible spatial constraints based on factor analysis”, Frontiers of Computer Science, vol.10 (2), pp. 302-316, 2016.
[25] O. Zoidi, A. Tefas, and I. Pitas. "Multiplicative update rules for concurrent nonnegative matrix factorization and maximum margin classification." IEEE transactions on neural networks and learning systems 24, no. 3 pp. 422-434, 2013.
[26] 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.
[27] C. Ding, T. Li, and M. Jordan, “Convex and semi-nonnegative matrix factorizations for clustering and low-dimension representation”, Technical Report LBNL-60428, Lawrence Berkeley National Laboratory, University of California, Berkeley, 2006.
[28] Martinez, A.M. The AR Face Database, “CVC Technical Report”, Volume 24, The Ohio State University: Colombus, OH, USA, 1998.
[29] https://onlinecourses.science.psu.edu/stat857/book/export/html/211
[29] Chris J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recogntion”, Data Mining and Knowledge Discovery Journal, pp. 121-167, vol. 2, 1998.

指導教授

王家慶(Wang Jia-Ching)

審核日期

2017-7-27

推文