Codes and Datasets for Feature Learning
Dimensionality reduction (Subspace learning) / Feature selection / Topic modeling / Matrix factorization / Sparse coding / Hashing / Clustering / Active learning
We provide here some 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
All the codes are on the GitHub.
Popular data sets used in our papers
Reproducing experimental results in some of my papers
- D. Cai, "A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search", arXiv 2018
- C. Fu et al, "Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph", VLDB 2019.
- D. Deng et al, "PixelLink: Detecting Scene Text via Instance Segmentation", AAAI 2018
- Y. Zhu et al, "A Brand-level Ranking System with the Customized Attention-GRU Model", IJCAI 2018
- H. Xue et al, "A Better Way to Attend: Attention With Trees for Video Question Answering", IEEE TIP 2018.
- J. Li et al, "Deep Rotation Equivariant Network", Neurocomputing 2018.
- Y. Zhu et al, "What to Do Next: Modeling User Behaviors by Time-LSTM", IJCAI 2017
- W. Zhang et al, "Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction", ICML 2017.
- W. Zhang et al, "Sparse Learning with Stochastic Composite Optimization", IEEE TPAMI 2017.
- H. Xue et al, "Depth Image Inpainting: Improving Low Rank Matrix Completion With Low Gradient Regularization", IEEE TIP 2017.
- H. Xue et al, "Unifying the Video and Question Attentions for Open-Ended Video Question Answering", IEEE TIP 2017.
- C. Fu et al, "EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph", arXiv 2016.
- W. Qian et al., "Non-Negative Matrix Factorization with Sinkhorn Distance", IJCAI 2016.
- 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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