Incremental shared subspace learning for multi-label classification

Authors:
Lei Zhang;Yao Zhao;Zhenfeng Zhu
Affiliations:
Institute of Information Science, Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University Beijing, Beijing, China;Institute of Information Science, Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University Beijing, Beijing, China;Institute of Information Science, Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University Beijing, Beijing, China
Venue:
CVM'12 Proceedings of the First international conference on Computational Visual Media
Year:
2012

Citing 5
Cited 0

A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
A Comparative Study on Feature Selection in Text Categorization

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchical multi-label prediction of gene function

Bioinformatics
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data

The Journal of Machine Learning Research
Extracting shared subspace for multi-label classification

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multi-label classification plays an increasingly significant role in most applications, such as semantic scene classification. In order to exploit the related information hidden in different labels which is crucial for lots of applications, it is essential to extract a latent structure shared among different labels. This paper presents an incremental approach for extracting a shared subspace on dynamic dataset. With the incremental lossless matrix factorization, the proposed algorithm can be incrementally performed without using original existing input data so that to avoid high computational complexity and decreasing the predictive performance. Experimental results demonstrate that the proposed approach is much more efficient than the non-incremental methods.