NMF-Based Comprehensive Latent Factor Learning with Multiview da

Authors: Zheng, H., Liang, Z., Tian, F. and Ming, Z.

Journal: Proceedings - International Conference on Image Processing, ICIP

Volume: 2019-September

Pages: 489-493

ISBN: 9781538662496

ISSN: 1522-4880

DOI: 10.1109/ICIP.2019.8803837

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.

http://eprints.bournemouth.ac.uk/33210/

Source: Scopus

NMF-BASED COMPREHENSIVE LATENT FACTOR LEARNING WITH MULTIVIEW DATA

Authors: Zheng, H., Liang, Z., Tian, F., Ming, Z. and IEEE

Journal: 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)

Pages: 489-493

ISSN: 1522-4880

http://eprints.bournemouth.ac.uk/33210/

Source: Web of Science (Lite)