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• (Reviewed by Chiho Choi) Summary This slecture presents a mathematical concept of Principal Component Analysis (PCA) and its practical applications. In section 2, the author explains about basic linear algebra, such as eigenvectors, eigenvalues, and singular vector decomposition, which are required to understand how PCA works. Section 3 shows the way of projecting high dimensional data to lower dimensional space based on the concepts of section 2. Then, he/she demonstrates it using 2D data in Section 4.1 and 512x512 image in Section 4.2, respectively. Section 4.3 provides some limitations in such a case that PCA fails to reduce data dimensions as shown in Figure 8 – 13.

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