March 2018
Beginner to intermediate
306 pages
9h 54m
English
Content preview from OpenCV 3 Computer Vision with Python Cookbook
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
Start your free trial
Rank-constrained matrix approximation
In this recipe, you will learn how to compute a rank-considerant matrix approximation. The problem is formulated as an optimization problem. Given an input matrix, the aim is to find its approximation where the fit is measured using the Frobenius norm and the rank of the output matrix should not be greater than the given value. This functionality, among other fields, is used in data compression and machine learning.