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Dimensionality Reduction
ML Math Derivations (17): Dimensionality Reduction and PCA
High-dimensional spaces are hostile to distance-based learning. This article derives PCA from two equivalent angles (max variance and min reconstruction error), and extends to kernel PCA, LDA, t-SNE, and ICA -- with …
Singular Value Decomposition -- The Crown Jewel of Linear Algebra
SVD decomposes any matrix -- not just square or symmetric ones. From image compression to Netflix recommendations, from face recognition to gene analysis, SVD is the most powerful and most universal decomposition in …