搜索资源列表
PCALDA
- PCA+LDA经典人脸识别算法，先用PCA降维，再用LCA降维-PCA+ LDA classical face recognition algorithms, first PCA dimension reduction, reuse LCA dimension reduction
lda
- 非线性降维方法 可以应用于高维数据的机器学习-Nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
lda
- 一个基于人耳模式识别的lda算法，可实现对高维矩阵的降维。-A pattern recognition based on human ear lda algorithm can realize high-dimensional matrix of dimension reduction.
LPP
- 很好的降维算法解析，主要讲述了LPP、PCA、LDA算法，希望对大家有帮助-Dimensionality reduction algorithm analysis focuses on the LPP, PCA, LDA algorithm, we hope to help
drtoolbox
- Matlab针对各种数据预处理的降维方法，源码集合。-Currently, the Matlab Toolbox for Dimensionality Reduction contains the following techniques: Principal Component Analysis (PCA) Probabilistic PCA Factor Analysis (FA) Sammon mapping Lin
dimension-reduction
- pca lda kpca klda lpp matlab 特征降为方法，对于新手比较有用。-pca lda kpca klda lpp matlab code for dimension reduction. these are very useful for new researchers.
LDA1
- LDA线性判别算法，可以用来对数据进行降维(LDA linear discriminant algorithm can be used for dimensionality reduction of data.)