Generalized Low Rank Models
by Madeleine Udell, Corinne Horn, Reza Zadeh, Stephen Boyd · first published 2016
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…finance, ML, etc.) and eschews more arcane concepts (like eigen). To me, building practical intuition is the best way to learn* the subject. A more advanced but still accessible manuscript by Boyd et al: Generalized Low Rank Models (establishes connections between PCA/SVD and many other matrix factorization methods, and shows you how to roll your own): https://web.stanford.edu/~boyd/papers/pdf/glrm.pdf
on “CS 168: The Modern Algorithmic Toolbox” ↗…(it is a square norm) by 0 and you achieve 0 loss, this means that 0 is a global optimum, since, by definition, no other objective value can be smaller than this number. --- [0] Theorem A.2 in Udell's Generalized Low-Rank models paper https://arxiv.org/pdf/1410.0342.pdf [1] B&V Convex Optimization (https://web.stanford.edu/~boyd/cvxbook/), Appendix B.1. In fact, I can't find the reference right now, but you can easily prove…
on “Gradient Descent Finds Global Minima of Deep Neural Networks” ↗