NMFS algorithm (nonnegative sparse matrix factorization with sparsity constraint)
关注次数： 315
下载次数： 0
文件大小： 22.26M
中文说明：NMFs算法（带稀疏度约束的非负稀疏矩阵分解）用于实现基于人脸局部特征的人脸识别，通过近似的矩阵分解进行空间降维。
English Description：
NMFS (nonnegative sparse matrix factorization with sparsity constraint) is used to realize face recognition based on local features of human face, and spatial dimension is reduced by approximate matrix decomposition.nmfpack
.......\code
.......\....\cbcldata.m
.......\....\lnmf.m
.......\....\main.m
.......\....\natdata.m
.......\....\nmfdiv.m
.......\....\nmfmse.m
.......\....\nmfsc.m
.......\....\orldata.m
.......\....\pgma_read.m
.......\....\projfunc.m
.......\....\projtest.m
.......\....\sampleimages.m
.......\....\snmf.m
.......\....\visual.m
.......\data
.......\....\cbcl-face-database
.......\....\IMAGES.mat
.......\....\orl-faces
.......\....\.........\htm" target=_blank>README
.......\....\.........\s1
.......\....\.........\..\1.pgm
.......\....\.........\..\10.pgm
.......\....\.........\..\2.pgm
.......\....\.........\..\3.pgm
.......\....\.........\..\4.pgm
.......\....\.........\..\5.pgm
.......\....\.........\..\6.pgm
.......\....\.........\..\7.pgm
.......\....\.........\..\8.pgm
.......\....\.........\..\9.pgm
.......\....\.........\s10
.......\....\.........\...\1.pgm
.......\....\.........\...\10.pgm
.......\....\.........\...\2.pgm
.......\....\.........\...\3.pgm
.......\....\.........\...\4.pgm
.......\....\.........\...\5.pgm
.......\....\.........\...\6.pgm
.......\....\.........\...\7.pgm
.......\....\.........\...\8.pgm
.......\....\.........\...\9.pgm
.......\....\.........\...\jpg
.......\....\.........\s11
.......\....\.........\...\1.pgm
.......\....\.........\...\10.pgm
.......\....\.........\...\2.pgm
.......\....\.........\...\3.pgm
.......\....\.........\...\4.pgm
.......\....\.........\...\5.pgm
.......\....\.........\...\6.pgm
.......\....\.........\...\7.pgm
.......\....\.........\...\8.pgm
.......\....\.........\...\9.pgm
.......\....\.........\s12
.......\....\.........\...\1.pgm
.......\....\.........\...\10.pgm
.......\....\.........\...\2.pgm
.......\....\.........\...\3.pgm
.......\....\.........\...\4.pgm
.......\....\.........\...\5.pgm
.......\....\.........\...\6.pgm
.......\....\.........\...\7.pgm
.......\....\.........\...\8.pgm
.......\....\.........\...\9.pgm
.......\....\.........\s13
.......\....\.........\...\1.pgm
.......\....\.........\...\10.pgm
.......\....\.........\...\2.pgm
.......\....\.........\...\3.pgm
.......\....\.........\...\4.pgm
.......\....\.........\...\5.pgm
.......\....\.........\...\6.pgm
.......\....\.........\...\7.pgm
.......\....\.........\...\8.pgm
.......\....\.........\...\9.pgm
.......\....\.........\s14
.......\....\.........\...\1.pgm
.......\....\.........\...\10.pgm
.......\....\.........\...\2.pgm
.......\....\.........\...\3.pgm
.......\....\.........\...\4.pgm
.......\....\.........\...\5.pgm
.......\....\.........\...\6.pgm
.......\....\.........\...\7.pgm
.......\....\.........\...\8.pgm
.......\....\.........\...\9.pgm
.......\....\.........\s15
.......\....\.........\...\1.pgm
.......\....\.........\...\10.pgm
.......\....\.........\...\2.pgm
.......\....\.........\...\3.pgm
.......\....\.........\...\4.pgm
.......\....\.........\...\5.pgm
.......\....\.........\...\6.pgm
.......\....\.........\...\7.pgm
.......\....\.........\...\8.pgm
.......\....\.........\...\9.pgm