Explore >> Select a destination
|
You are here 
|
networkscience.wordpress.com | ||
| | | | |
blog.georgeshakan.com
|
|
| | | | | 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... | |
| | | | |
nickhar.wordpress.com
|
|
| | | | | 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... | |
| | | | |
adam.younglogic.com
|
|
| | | | | [AI summary] The article discusses an algorithm for parallelizing matrix-vector multiplication by decomposing the computation into smaller chunks to enable parallel processing. | |
| | | | |
ecollections.law.fiu.edu
|
|
| | | By Dr. Matthew Eric Bassett, Published on 01/01/20 | ||
