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

NMF-based Comprehensive Latent Factor Learning with Multiview Data

Citation Author(s):
Hua Zheng, Zhixuan Liang, Feng Tian, Zhong Ming
Submitted by:
Hua Zheng
Last updated:
11 September 2019 - 2:26am
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Zhixuan Liang
Paper Code:
3099

Abstract 

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:

Dataset Files

ICIPNMF-based CLFL

(187)