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marc-b-reynolds.github.io | ||
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blog.georgeshakan.com
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| | | | | Principal Component Analysis (PCA) is a popular technique in machine learning for dimension reduction. It can be derived from Singular Value Decomposition (SVD) which we will discuss in this post. We will cover the math, an example in python, and finally some intuition. The Math SVD asserts that any $latex m \times d$ matrix $latex... | |
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www.johndcook.com
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| | | | | You can use quaternions to describe rotations and quaternion products to carry out these rotations. These products have the form qpq* where q represents a rotation, q* is its conjugate, and p is the the vector being rotated. (That's leaving out some details that we'll get to shortly.) The primary advantage of using quaternions to | |
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francisbach.com
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| | | | | [AI summary] This mathematical post explores the geometry of positive semi-definite matrices using the von Neumann entropy and related Bregman divergences to derive concentration inequalities for random matrices. | |
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qchu.wordpress.com
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| | | As a warm-up to the subject of this blog post, consider the problem of how to classify$latex n \times m$ matrices $latex M \in \mathbb{R}^{n \times m}$ up to change of basis in both the source ($latex \mathbb{R}^m$) and the target ($latex \mathbb{R}^n$). In other words, the problem is todescribe the equivalence classes of the... | ||
