主成分分析(PCA)是一種常見且熱門的降維方法。然而，當我們想通過主成 分的載荷係數(loading)進一步對資料與變數之間做推論時，常因載荷係數不為零 而難以得到簡易的解釋結果。本文的主要目的是嘗試透過貝氏方法得到 SPCA 標 準下的稀疏載荷，其中我們使用一種全部局部收縮先驗(global-local shrinkage prior)模型 MBSP-TPBN。數值模擬和實際數據證明本文提出的方法。;Principal component analysis (PCA) is a common and popular dimensionality reduc- tion method. However, when we want to make further inferences between data and variables through the loadings of principal components, it is often difficult to obtain simple interpretation results due to all non-zero loadings. The main purpose of this thesis is to try to obtain the sparse loadings under the SPCA criterion based on a Bayesian approach, in which we use a global-local shrinkage prior model MBSP-TPBN. The numerical study and real data demonstrate the proposed method in the article.
