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theorydish.blog | ||
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nickhar.wordpress.com
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| | | | | 1. Low-rank approximation of matrices Let $latex {A}&fg=000000$ be an arbitrary $latex {n \times m}&fg=000000$ matrix. We assume $latex {n \leq m}&fg=000000$. We consider the problem of approximating $latex {A}&fg=000000$ by a low-rank matrix. For example, we could seek to find a rank $latex {s}&fg=000000$ matrix $latex {B}&fg=000000$ minimizing $latex { \lVert A - B... | |
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francisbach.com
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polymathprojects.org
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| | | | | It's probably time to refresh the previous thread for the "finding primes" project, and to summarise the current state of affairs. The current goal is to find a deterministic way to locate a prime in an interval $latex [z,2z]$ in time that breaks the "square root barrier" of $latex \sqrt(z)$ (or more precisely, $latex z^{1/2+o(1)}$).... | |
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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... | ||
