集体智慧编程书中的一些关键概念

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#编程 #matrix #constraints #methods #variables

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本文介绍了非负矩阵分解（NMF）及其在数据特征识别中的应用，包括其原理、实现方式以及如何通过非负约束来获取有意义的数据表示。

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1 Scaling and superfluous variables

对某些变量值放大，而对另一些值缩小，使整体数据间具有可比性

2 Multimentional Scaling

可视化数据间的关联关系

3 非负矩阵分解

Non-negative matrix factorization

用以帮助我们识别数据的特征

来源：集体智慧编程中文版

Appendix：

A 非负矩阵分解补充(wiki)

Non-negative matrix factorization

From Wikipedia, the free encyclopedia

NMF redirects here. For the bridge convention, see new minor forcing.

Non-negative matrix factorization (NMF) is a group of algorithms in multivariate analysis and linear algebra where a matrix, $\mathbf{X}$ , is factorized into (usually) two matrices, $\mathbf{W}$ and $\mathbf{H}$ : $\operatorname{nmf}(\mathbf{X}) \rightarrow \mathbf{WH}$

Factorization of matrices is generally non-unique, and a number of different methods of doing so have been developed (e.g. principal component analysis and singular value decomposition) by incorporating different constraints; non-negative matrix factorization differs from these methods in that it enforces the constraint that the factors W and H must be non-negative, i.e., all elements must be equal to or greater than zero.

来源2： http://en.wikipedia.org/wiki/Non-negative_matrix_factorization

B 非负张量分解

http://www.cc.gatech.edu/~hpark/nmfsoftware.php