Tagged
PCA
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 …
Essence of Linear Algebra (15): Linear Algebra in Machine Learning
Machine learning speaks linear algebra as its native language. From PCA to SVMs, from matrix factorization in recommender systems to gradient descent optimization -- see how vectors, matrices, and decompositions power …
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 …