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• W2948382333 abstract "This is a detailed tutorial paper which explains the Principal Component Analysis (PCA), Supervised PCA (SPCA), kernel PCA, and kernel SPCA. We start with projection, PCA with eigen-decomposition, PCA with one and multiple projection directions, properties of the projection matrix, reconstruction error minimization, and we connect to auto-encoder. Then, PCA with singular value decomposition, dual PCA, and kernel PCA are covered. SPCA using both scoring and Hilbert-Schmidt independence criterion are explained. Kernel SPCA using both direct and dual approaches are then introduced. We cover all cases of projection and reconstruction of training and out-of-sample data. Finally, some simulations are provided on Frey and AT&T face datasets for verifying the theory in practice." @default.
• W2948382333 created "2019-06-14" @default.
• W2948382333 creator A5024030325 @default.
• W2948382333 creator A5064795613 @default.
• W2948382333 date "2019-06-01" @default.
• W2948382333 modified "2023-09-27" @default.
• W2948382333 title "Unsupervised and Supervised Principal Component Analysis: Tutorial." @default.
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