Data sets for multi-view learning in MatLab ".mat" file format.
##Introduction
This repository contains the data that is used in the following paper.
Li, Yeqing, Feiping Nie, Heng Huang, and Junzhou Huang. "Large-Scale Multi-View Spectral Clustering via Bipartite Graph." In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.
If you use the data, please cite our paper:
@inproceedings{li2015large,
title={Large-Scale Multi-View Spectral Clustering via Bipartite Graph},
author={Li, Yeqing and Nie, Feiping and Huang, Heng and Huang, Junzhou},
booktitle={Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence},
year={2015}
}
###How to use
The ".mat" files using the following script.
sh get_data.sh
Note: The whole dataset is about 300MB to 400MB.
###About the data format
All the data is stored in MatLab ".mat" file format. Each ".mat" file contains at least two variables: X and Y. "X" is a cell array of feature matrix (dimension N by d) and "Y" is the label vector (dimension N by 1), where N is the number of data points and d is the dimension of features. The specification of the data is listed below
| No. | Handwritten | Caltech-7/20 | Reuters | NUS-WIDE | AWA |
|---|---|---|---|---|---|
| 1 | Pix(240) | Gabor(48) | English(21531) | CH(65) | CQ(2688) |
| 2 | Fou(76) | WM(40) | France(24892) | CM(226) | LSS(2000) |
| 3 | Fac(216) | CENTRIST(254) | German(34251) | CORR(145) | PHOG(252) |
| 4 | ZER(47) | HOG(1984) | Italian(15506) | EDH(74) | SIFT(2000) |
| 5 | KAR(64) | GIST(512) | Spanish(11547) | WT(129) | RGSIFT(2000) |
| 6 | MOR(6) | LBP(928) | - | - | SURF(2000) |
| num of data | 2000 | 1474/2386 | 18758 | 26315 | 4000 |
| num of classes | 10 | 7/20 | 6 | 31 | 50 |
Note: Caltech-7 and Caltech-20 are two subsets of Caltech-101, which contains only 7 and 20 classes respectively. The creation of these subsets is due to the unbalance of the number of data in each classes of Caltech-101.
List of features:
- Fourier coefficients of the character shapes (FOU)
- Profile correlations (FAC)
- Pixel averages in 2 × 3 windows (Pix),
- Zernike moment (ZER)
- Morphological (MOR) features.
- Gabor feature
- Wavelet moments (WM)
- CENTRIST feature
- Histogram of oriented gradients (HOG) feature
- GIST feature
- Local binary patterns (LBP) feature
- Color Histogram (CH)
- Color moments (CM)
- Color correlation (CORR)
- Edge distribution (EDH)
- Wavelet texture (WT)
##Source
The source of the data are downloaded from the following links: