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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:
Zhixuan Liang
Paper Code:
3099
 

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.

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