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.
https://eprints.bournemouth.ac.uk/33210/
Source: Scopus
NMF-BASED COMPREHENSIVE LATENT FACTOR LEARNING WITH MULTIVIEW DATA
Authors: Zheng, H., Liang, Z., Tian, F. and Ming, Z.
Journal: 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Pages: 489-493
ISSN: 1522-4880
https://eprints.bournemouth.ac.uk/33210/
Source: Web of Science (Lite)
NMF-Based Comprehensive Latent Factor Learning with Multiview da
Authors: Zheng, H., Liang, Z., Tian, F. and Ming, Z.
Conference: IEEE International Conference on Image Processing (ICIP)
Pages: 489-493
ISBN: 9781538662496
ISSN: 1522-4880
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.
https://eprints.bournemouth.ac.uk/33210/
Source: BURO EPrints