Awesome
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.