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NMF-Based Comprehensive Latent Factor Learning with Multiview da.

Zheng, H., Liang, Z., Tian, F. and Ming, Z., 2019. NMF-Based Comprehensive Latent Factor Learning with Multiview da. In: IEEE International Conference on Image Processing (ICIP), 22-25 September 2019, Taipei, Taiwan, 489 - 493.

Full text available as:

08803837.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/ICIP.2019.8803837


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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Comprehensive multiview learning; latent factor learning; non-negative matrix factorization (NMF)
Group:Faculty of Science & Technology
ID Code:33210
Deposited By: Symplectic RT2
Deposited On:08 Jan 2020 12:18
Last Modified:14 Mar 2022 14:19


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