Deep Sparse Regularizer Learning (DSRL)

This is an implement of methods in "Learning Deep Sparse Regularizers with Applications to Multi-View Clustering and Semi-Supervised Classification" that published in IEEE Trans. Pattern Analysis and Machine Intelligence.

Datasets Descriptions

• Original similarity matrices are stored in /datasetW, which are generated by KNN (see Matlab codes in ConstructW.zip).
• Original multi-view datasets are stored in /_multiview datasets.

Environment

Require Python 3.8

• torch 1.8.0
• numpy 1.16.3
• tqdm 4.28.1
• scikit-learn 0.20.3

Quick Running

• Run python ./run_Clustering.py --dataset-id 1 for clustering tasks.
• Run python ./run_Classification.py --dataset-id 1 for semi-supervised classification tasks.
• Note: dataset-id values of all presented datasets are as shown below:
  • 1:'ALOI', 2:'Caltech101-7', 3:'Caltech101-20', 4:'MNIST', 5:'MSRC-v1', 6:'NUS-WIDE', 7:'Youtube', 8:'ORL'

Citation

• Shiping Wang, Zhaoliang Chen, Shide Du, and Zhouchen Lin, Learning Deep Sparse Regularizers with Applications to Multi-View Clustering and Semi-Supervised Classification, IEEE Trans. Pattern Analysis and Machine Intelligence, 2021.