Multi-View Feature Learning Based on User Contributed Tag

Authors

Tian Feng, 1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China;;
Fuhua Shang, 1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China;;
Zhuoxuan Liu, 1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China;;
Xukun Shen, 2. The State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;

Abstract

Abstract: A multi-view feature learning method based on user contributed tag was proposed. Bag-of-words representation for content feature and textual feature was learned. A multi-view feature learning framework was proposed to explicitly model the relevance between multimedia object and tags by learning a linear mapping from textual representation to visual representation. The learned feature encoded the information conveyed by original feature, and inner products of leaned features were preserved with a high probability with visual features and textual features. The complexity of the method is linear with respect to the size of dataset. Furthermore, the method can be extended to deal with more than two views. The performance of the proposed method indicts it’s superiority over other representative method.

Recommended Citation

Feng, Tian; Shang, Fuhua; Liu, Zhuoxuan; and Shen, Xukun (2020) "Multi-View Feature Learning Based on User Contributed Tag," Journal of System Simulation: Vol. 28: Iss. 10, Article 11.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss10/11

First Page

2362

Revised Date

2016-07-14

DOI Link

https://doi.org/

Last Page

2368

CLC

TP391

Recommended Citation

Tian Feng, Shang Fuhua, Liu Zhuoxuan, Shen Xukun. Multi-View Feature Learning Based on User Contributed Tag[J]. Journal of System Simulation, 2016, 28(10): 2362-2368.