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www.ethanepperly.com | ||
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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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stephenmalina.com
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| | | | | Matrix Potpourri # As part of reviewing Linear Algebra for my Machine Learning class, I've noticed there's a bunch of matrix terminology that I didn't encounter during my proof-based self-study of LA from Linear Algebra Done Right. This post is mostly intended to consolidate my own understanding and to act as a reference to future me, but if it also helps others in a similar position, that's even better! | |
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nhigham.com
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| | | | | The spectral radius $latex \rho(A)$ of a square matrix $latex A\in\mathbb{C}^{n\times n}$ is the largest absolute value of any eigenvalue of $LATEX A$: $latex \notag \rho(A) = \max\{\, |\lambda|: \lambda~ \mbox{is an eigenvalue of}~ A\,\}. $ For Hermitian matrices (or more generally normal matrices, those satisfying $LATEX AA^* = A^*A$) the spectral radius is just... | |
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scholarcommons.usf.edu
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| | | Despite nearly two decades of research, researchers have not resolved whether people generally perceive their skills accurately or inaccurately. In this paper, we trace this lack of resolution to numeracy, specifically to the frequently overlooked complications that arise from the noisy data produced by the paired measures that researchers employ to determine self-assessment accuracy. To illustrate the complications and ways to resolve them, we employ a large dataset (N = 1154) obtained from paired measu... | ||
