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Xifeng Guo

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5 papers

1 author row

AAAI Conference 2021 Conference Paper

Deep Fusion Clustering Network

Wenxuan Tu
Sihang Zhou
Xinwang Liu
Xifeng Guo
Zhiping Cai
En Zhu
Jieren Cheng

Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i. e. , “groundtruth” soft labels) generation. To tackle the above issues, we propose a Deep Fusion Clustering Network (DFCN). Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning. Also, a reliable target distribution generation measure and a triplet self-supervision strategy, which facilitate cross-modality information exploitation, are designed for network training. Extensive experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods. Our code is publicly available at https: //github. com/WxTu/DFCN.

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AAAI Conference 2020 Conference Paper

Multi-View Spectral Clustering with Optimal Neighborhood Laplacian Matrix

Sihang Zhou
Xinwang Liu
Jiyuan Liu
Xifeng Guo
Yawei Zhao
En Zhu
Yongping Zhai
Jianping Yin

Multi-view spectral clustering aims to group data into different categories by optimally exploring complementary information from multiple Laplacian matrices. However, existing methods usually linearly combine a group of pre-speciﬁed ﬁrst-order Laplacian matrices to construct an optimal Laplacian matrix, which may result in limited representation capability and insufﬁcient information exploitation. In this paper, we propose a novel optimal neighborhood multi-view spectral clustering (ONMSC) algorithm to address these issues. Speciﬁcally, the proposed algorithm generates an optimal Laplacian matrix by searching the neighborhood of both the linear combination of the ﬁrst-order and high-order base Laplacian matrices simultaneously. This design enhances the representative capacity of the optimal Laplacian and better utilizes the hidden high-order connection information, leading to improved clustering performance. An efﬁcient algorithm with proved convergence is designed to solve the resultant optimization problem. Extensive experimental results on 9 datasets demonstrate the superiority of our algorithm against state-of-the-art methods, which veriﬁes the effectiveness and advantages of the proposed ONMSC.

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IJCAI Conference 2019 Conference Paper

Affine Equivariant Autoencoder

Xifeng Guo
En Zhu
Xinwang Liu
Jianping Yin

Existing deep neural networks mainly focus on learning transformation invariant features. However, it is the equivariant features that are more adequate for general purpose tasks. Unfortunately, few work has been devoted to learning equivariant features. To fill this gap, in this paper, we propose an affine equivariant autoencoder to learn features that are equivariant to the affine transformation in an unsupervised manner. The objective consists of the self-reconstruction of the original example and affine transformed example, and the approximation of the affine transformation function, where the reconstruction makes the encoder a valid feature extractor and the approximation encourages the equivariance. Extensive experiments are conducted to validate the equivariance and discriminative ability of the features learned by our affine equivariant autoencoder.

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AAAI Conference 2018 Short Paper

Deep Embedding for Determining the Number of Clusters

Yiqi Wang
Zhan Shi
Xifeng Guo
Xinwang Liu
En Zhu
Jianping Yin

Determining the number of clusters is important but challenging, especially for data of high dimension. In this paper, we propose Deep Embedding Determination (DED), a method that can solve jointly for the unknown number of clusters and feature extraction. DED ﬁrst combines the virtues of the convolutional autoencoder and the t-SNE technique to extract low dimensional embedded features. Then it determines the number of clusters using an improved density-based clustering algorithm. Our experimental evaluation on image datasets shows signiﬁcant improvement over state-of-the-art methods and robustness with respect to hyperparameter settings.

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IJCAI Conference 2017 Conference Paper

Improved Deep Embedded Clustering with Local Structure Preservation

Xifeng Guo
Long Gao
Xinwang Liu
Jianping Yin

Deep clustering learns deep feature representations that favor clustering task using neural networks. Some pioneering work proposes to simultaneously learn embedded features and perform clustering by explicitly defining a clustering oriented loss. Though promising performance has been demonstrated in various applications, we observe that a vital ingredient has been overlooked by these work that the defined clustering loss may corrupt feature space, which leads to non-representative meaningless features and this in turn hurts clustering performance. To address this issue, in this paper, we propose the Improved Deep Embedded Clustering (IDEC) algorithm to take care of data structure preservation. Specifically, we manipulate feature space to scatter data points using a clustering loss as guidance. To constrain the manipulation and maintain the local structure of data generating distribution, an under-complete autoencoder is applied. By integrating the clustering loss and autoencoder's reconstruction loss, IDEC can jointly optimize cluster labels assignment and learn features that are suitable for clustering with local structure preservation. The resultant optimization problem can be effectively solved by mini-batch stochastic gradient descent and backpropagation. Experiments on image and text datasets empirically validate the importance of local structure preservation and the effectiveness of our algorithm.

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