特征学习的matlab代码和数据集 Matlab Codes and Datasets for Feature Learning

Matlab Codes and Datasets for Feature Learning

Dimensionality reduction (Subspace learning) / Feature selection / Topic modeling / Matrix factorization / Sparse coding / Hashing / Clustering / Active learning

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We provide here some matlab codes of feature learning algorithms, as well as some datasets in matlab format. All these codes and data sets are used in our experiments. The processed data in matlab format can only be used for non-commercial purpose. 

If you have some problems or find some bugs in the codes, please email: dengcai  AT  gmail  DOT  com

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The codes on this site have been regularly updated (bug fixed, functions added ...). The simplest way to keep updated is downloading this entire package (updated on 2012/2/27) .

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Maltlab codes

• Spectral regression : (a regression framework for efficient dimensionality reduction)
• Dimensionality reduction (Subspace learning)
• Feature selection
• Topic modeling and GMM
• Matrix factorization
• Sparse coding
• Hashing
• Clustering
• Active learning
• Ranking and Metric learning

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Popular data sets used in our papers

• Face databases (Yale, ORL, PIE and YaleB)
• Text data sets
• Other standard data sets
• Depth Inpainting database

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Reproducing experimental results in some of my papers

• D. Cai et al., "Manifold Adaptive Experimental Design for Text Categorization", IEEE TKDE 2012.
• B. Xu et al., "Efficient manifold ranking for image retrieval", SIGIR 2011.
• D. Cai et al., "Sparse Concept Coding for Visual Analysis", CVPR 2011.
• X. Chen et al., "Large Scale Spectral Clustering with Landmark-Based Representation," AAAI 2011.
• D. Cai et al., "Graph Regularized Non-negative Matrix Factorization for Data Representation", IEEE TPAMI 2011.
• D. Cai et al., "Locally Consistent Concept Factorization for Document Clustering", IEEE TKDE 2011.
• D. Cai et al., "Speed Up Kernel Discriminant Analysis", The VLDB Journal, 2011.
• M. Zheng et al., "Graph Regularized Sparse Coding for Image Representation", IEEE TIP 2011.
• D. Cai et al., "Unsupervised Feature Selection for Multi-cluster Data," KDD 2010.
• J. Liu et al., "Gaussian Mixture Model with Local Consistency ," AAAI 2010.
• D. Cai et al., "Locality Preserving Nonnegative Matrix Factorization", IJCAI 2009.
• D. Cai et al., "Probabilistic Dyadic Data Analysis with Local and Global Consistency", ICML 2009.
• D. Cai et al., "Non-negative Matrix Factorization on Manifold", ICDM 2008.
• D. Cai et al., "Modeling Hidden Topics on Document Manifold", CIKM 2008.
• D. Cai et al., "SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis", IEEE TKDE 2008.
• D. Cai et al., "Spectral Regression: A Unified Approach for Sparse Subspace Learning ", ICDM 2007.
• D. Cai et al., "Efficient Kernel Discriminant Analysis via Spectral Regression", ICDM 2007.
• D. Cai et al., "Semi-supervised Discriminant Analysis", in ICCV'07.
• D. Cai et al., "Spectral Regression for Efficient Regularized Subspace Learning", in ICCV'07
• D. Cai et al., "Regularized Locality Preserving Indexing via Spectral Regression", in CIKM'07.
• D. Cai et al., "Learning a Spatially Smooth Subspace for Face Recognition", in CVPR'07
• D. Cai, et al., "Orthogonal Laplacianfaces for Face Recognition", IEEE Trans. on Image Processing, 2006.
• X. He et al., "Tensor Subspace Analysis", in NIPS'05
• X. He et al., "Laplacian Score for Feature Selection", in NIPS'05

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from: http://www.cad.zju.edu.cn/home/dengcai/Data/data.html