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用于人脸识别的NMF分解程序,用MATLAB编写的
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文件大小: 13mb
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上传时间: 2011-12-23
详细说明： ---------------------------------------------------------------------------- README file for nmfpack ---------------------------------------------------------------------------- Patrik Hoyer August 03, 2006 Version 1.1 This Matlab package implements and tests various versions of non-negative matrix factorization (NMF). This package is associated with the article: Patrik O Hoyer. 'Non-negative matrix factorization with sparseness constraints' Journal of Machine Learning Research 5:1457-1469, 2004. IF YOU USE THIS PACKAGE IN PUBLISHED RESEARCH, PLEASE CITE THE ABOVE PAPER. THANKS! ---------------------------------------------------------------------------- Version history ---------------------------------------------------------------------------- 1.0 Aug 25, 2004 Original version. 1.1 Aug 03, 2006 Modifications/improvements: - Included the ORL face dataset, with permission - Now automatically rescales the data so as to avoid overflow/underflow numerical problems - Included an alternative to using 'imread' to read the pgm image files. - Improved the documentation somewhat ---------------------------------------------------------------------------- Acknowledgements ---------------------------------------------------------------------------- I would like to thank Fabian Theis for contributing the pgma_read function, and Daniel Felps for pointing out that the face databases are no longer available on the net. I also thank Alastair Beresford who, on behalf of AT&T Cambridge Laboratories, granted me permission to include the ORL face image database in the package. Several people reported some problems with 'projfunc' producing imaginary values. This problem arises when the scale of the entries in the data matrix is too large. Thus, as of v1.1, I now renormalize the input matrix before starting computation. Thanks for reporting the problems. ---------------------------------------------------------------------------- List of contents of package ---------------------------------------------------------------------------- code/ main.m - main file for all NMF experiments nmfdiv.m - performs standard NMF with divergence objective nmfmse.m - performs standard NMF with euclidean objective nmfsc.m - performs NMF with sparseness constraints lnmf.m - performs Local NMF snmf.m - performs Sparse NMF projfunc.m - implements the L1/L2-norm projection projtest.m - tests the efficiency of the projection operator visual.m - displays an array of images cbcldata.m - reads the CBCL data (assuming it has been downloaded) orldata.m - reads the ORL data (assuming it has been downloaded) natdata.m - reads natural image data preprocessed into ON/OFF channels sampleimages.m - takes random patches from the natural image set pgma_read.m - reads PGM image files (alternative to 'imread') results/ - directory for storing results of various algorithms data/ cbcl-face-database/ - [put the cbcl data here after downloading] orl-faces/ - ORL face image database IMAGES.mat - natural images, high-pass filtered, provided by Bruno Olshausen as part of his 'sparsenet' matlab package ---------------------------------------------------------------------------- Using the package ---------------------------------------------------------------------------- DOWNLOADING THE CBCL FACE IMAGE DATABASE If you wish to perform the CBCL data experiments, start by downloading the images. The database can be found at: http://cbcl.mit.edu/cbcl/software-datasets/FaceData2.html These database should be uncompressed and put into its corresponding data directory. The directory structure should look like this: data/ cbcl-face-database/ README face/ face00001.pgm [...] face02429.pgm non-face/ [...] orl-faces/ README s1/ [...] s40/ Once you have the data installed, you are ready to start running experiments. RUNNING THE NMF EXPERIMENTS All experiments using the various versions of NMF are run by calling >> main('learn', expnum); where expnum is the number of the experiment. This number determines the dataset to be used, the algorithm to be applied, and the settings of all parameters. The expnum is decoded in the beginning of main.m, where you can inspect which experiments have which number and you can add your own. Please note that the learning phase never terminates! You have to terminate it yourself. I usually keep a second Matlab session open from which I can inspect the progress of the algorithm while it's running. From this second Matlab session, calling >> main('show', expnum); shows the evolution of the objective function, the current basis images, and some statistics of the representation (from top to bottom: utilization of the components, sparseness of basis images, and sparseness of the coefficients). The following settings duplicate the results of the paper 'Non-negative Matrix Factorization with sparseness constraints': Figure Corresponding in paper: expnum: --------------------- 1a 1 1b 101 1c 201 3a 11 3b 12 3c 13 4a 133 4b 134 4c 131 5 231 USING THE NMF FUNCTIONS DIRECTLY Of course, your data might not be image data, and you might not want to use the interface provided by main.m. In this case, call the various NMF routines directly. Again, note that there are no convergence criteria built in, but you can monitor the representation during learning because the routines save the results to disk periodically. USING/TESTING THE PROJECTION OPERATOR To reproduce Figure 6 in the paper, run projtest.m. It requires no parameters. To use the projection function, just call projfunc directly. Have a look at the file, the syntax should be self-explanatory. ---------------------------------------------------------------------------- Problems? ---------------------------------------------------------------------------- In case of questions or problems, do not hesitate to contact me, preferrably by email at patrik.hoyer@helsinki.fi. However, I would appreciate it if you would take the time to examine the code a little and try to figure out the problem yourself. If you cannot figure it out, at least try to provide as much info as possible on the nature of the problem (including your exact commands and the error messages), so that I can have a chance to help you. ---------------------------------------------------------------------------- Disclaimer ---------------------------------------------------------------------------- This software and data is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk! ...展开收缩
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