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Your search keyword '"Zhang, Hongyuan"' showing total 2 results
Start Over Author "Zhang, Hongyuan" Topic computational modeling Topic task analysis Publication Year Range Last 10 years
2 results on '"Zhang, Hongyuan"'

1. Maximum Joint Probability With Multiple Representations for Clustering.

Author

Zhang, Rui, Zhang, Hongyuan, and Li, Xuelong

Subjects
*PROBABILITY theory, *PROBABILISTIC generative models, *DATA distribution
Abstract

Classical generative models in unsupervised learning intend to maximize $p(X)$. In practice, samples may have multiple representations caused by various transformations, measurements, and so on. Therefore, it is crucial to integrate information from different representations, and lots of models have been developed. However, most of them fail to incorporate the prior information about data distribution $p(X)$ to distinguish representations. In this article, we propose a novel clustering framework that attempts to maximize the joint probability of data and parameters. Under this framework, the prior distribution can be employed to measure the rationality of diverse representations. $K$ -means is a special case of the proposed framework. Meanwhile, a specific clustering model considering both multiple kernels and multiple views is derived to verify the validity of the designed framework and model. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Robust Multi-Task Learning With Flexible Manifold Constraint.

Author

Zhang, Rui, Zhang, Hongyuan, and Li, Xuelong

Subjects
*TASK analysis
Abstract

Multi-Task Learning attempts to explore and mine the sufficient information within multiple related tasks for the better solutions. However, the performance of the existing multi-task approaches would largely degenerate when dealing with the polluted data, i.e., outliers. In this paper, we propose a novel robust multi-task model by incorporating a flexible manifold constraint (FMC-MTL) and a robust loss. Specifically speaking, multi-task subspace is embedded with a relaxed and generalized Stiefel Manifold for considering point-wise correlation and preserving the data structure simultaneously. In addition, a robust loss function is developed to ensure the robustness to outliers by smoothly interpolating between ℓ2,1-norm and squared Frobenius norm. Equipped with an efficient algorithm, FMC-MTL serves as a robust solution to tackling the severely polluted data. Moreover, extensive experiments are conducted to verify the superiority of our model. Compared to the state-of-the-art multi-task models, the proposed FMC-MTL model demonstrates remarkable robustness to the contaminated data. [ABSTRACT FROM AUTHOR]

Published
2021
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