HVAE: A deep generative model via hierarchical variational auto-encoder for multi-view document modeling

Article Computer Science, Information Systems

Neural generative model for clustering by separating particularity and commonality

Wenqing Wang et al.

Summary: Learning discriminative representation is essential in many machine learning tasks. In this study, we propose a generative model called the disentangling generative model for clustering (DGC) to improve clustering accuracy by separating particular and shared features. The DGC is optimized within the variational autoencoder framework and achieves comparable performance with state-of-the-art methods. The DGC is also capable of generating class-specific samples without label information.

INFORMATION SCIENCES (2022)

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Article Computer Science, Artificial Intelligence

Multi-View Representation Learning With Deep Gaussian Processes

Shiliang Sun et al.

Summary: The paper proposes a multi-view representation learning algorithm with deep Gaussian processes (MvDGPs), which can learn more effective representation of multi-view data. The algorithm consists of two stages: multi-view data representation learning and classifier design.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

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Article Computer Science, Information Systems

Deep multi-view document clustering with enhanced semantic embedding

Ruina Bai et al.

Summary: This paper introduces a novel model for multi-view document clustering using enhanced semantic embedding, which aims to address the difficulties of clustering text documents with multiple representations. Experimental results demonstrate that the proposed MDCE model performs substantially better than other state-of-the-art multi-view clustering approaches.

INFORMATION SCIENCES (2021)

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Article Computer Science, Artificial Intelligence

Multiview Variational Sparse Gaussian Processes

Liang Mao et al.

Summary: This article extends VSGP to handle multiview data and demonstrates that the MVSGP model consistently outperforms single-view VSGP and state-of-the-art kernel-based multiview baselines for classification tasks on real-world datasets.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

LCBM: A Multi-View Probabilistic Model for Multi-Label Classification

Shiliang Sun et al.

Summary: This paper introduces a new multi-view probabilistic model, LCBM, which leverages latent conditional Bernoulli mixture model to establish dependencies among labels, achieving effective modeling of label dependencies. Additionally, a scalable stochastic training algorithm is developed for efficiently optimizing model parameters and variational parameters, leading to experimental results that outperform other methods on multiple benchmark datasets.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

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Review Computer Science, Information Systems