xying0706/mvdata

Data sets for multi-view learning.

mvdata

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

Download from google drive using the browser.

• https://drive.google.com/drive/folders/1O_3YmthAZGiq1ZPSdE74R7Nwos2PmnHH

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:

• Handwritten
• Caltech-101
• Reuters
• NUS-WIDE-Object
• Animal with attributes (AWA)