Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

NMF-based Comprehensive Latent Factor Learning with Multiview Data

Abstract: 

Multiview representations reveal the latent information of the data from different perspectives, consistency, and complementarity. Unlike most multiview learning approaches, which focus only one perspective, in this paper, we propose a novel unsupervised multiview learning algorithm, called comprehensive latent factor learning (CLFL), which jointly exploits both consistent and complementary information among multiple views. CLFL adopts a non-negative matrix factorization based formulation to learn the latent factors. It learns the weights of different views automatically which makes the representation more accurate. Experiment results on a synthetic and several real datasets demonstrate the effectiveness of our approach.

up
0 users have voted:

Paper Details

Authors:
Hua Zheng, Zhixuan Liang, Feng Tian, Zhong Ming
Submitted On:
11 September 2019 - 2:26am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Zhixuan Liang
Paper Code:
3099
Document Year:
2019
Cite

Document Files

ICIPNMF-based CLFL

(14)

Subscribe

[1] Hua Zheng, Zhixuan Liang, Feng Tian, Zhong Ming, "NMF-based Comprehensive Latent Factor Learning with Multiview Data", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4588. Accessed: Sep. 15, 2019.
@article{4588-19,
url = {http://sigport.org/4588},
author = {Hua Zheng; Zhixuan Liang; Feng Tian; Zhong Ming },
publisher = {IEEE SigPort},
title = {NMF-based Comprehensive Latent Factor Learning with Multiview Data},
year = {2019} }
TY - EJOUR
T1 - NMF-based Comprehensive Latent Factor Learning with Multiview Data
AU - Hua Zheng; Zhixuan Liang; Feng Tian; Zhong Ming
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4588
ER -
Hua Zheng, Zhixuan Liang, Feng Tian, Zhong Ming. (2019). NMF-based Comprehensive Latent Factor Learning with Multiview Data. IEEE SigPort. http://sigport.org/4588
Hua Zheng, Zhixuan Liang, Feng Tian, Zhong Ming, 2019. NMF-based Comprehensive Latent Factor Learning with Multiview Data. Available at: http://sigport.org/4588.
Hua Zheng, Zhixuan Liang, Feng Tian, Zhong Ming. (2019). "NMF-based Comprehensive Latent Factor Learning with Multiview Data." Web.
1. Hua Zheng, Zhixuan Liang, Feng Tian, Zhong Ming. NMF-based Comprehensive Latent Factor Learning with Multiview Data [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4588