May 2015
Intermediate to advanced
246 pages
6h 33m
English
book
A Sampler of Useful Computational Tools for Applied Geometry, Computer Graphics, and Image Processing
by Daniel Cohen-Or, Chen Greif, Tao Ju, Niloy J. Mitra, Ariel Shamir, Olga Sorkine-Hornung, Hao Zhang
Content preview from A Sampler of Useful Computational Tools for Applied Geometry, Computer Graphics, and Image Processing
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Chapter 4
PCA and SVD
Olga Sorkine-Hornung
In this chapter, we introduce two related tools from linear alge-
bra that have become true workhorses in countless areas of scien-
tific computing: principal component analysis (PCA) and singular
value decomposition (SVD). Essentially, we will talk about a de-
composition of a given matrix into several factors that are easy to
analyze and reveal important properties of the matrix and hence
the data, or the problem in which the matrix arises. Surprisingly,
the singular value decomposition is often excluded from under-
graduate linear algebra curricula, even though it is so widely used
not only in geometric modeling ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.