Recently, multi-view high-dimensional data has drawn great attention due to its reflection of different properties or distributions and multi-view clustering have proven to be one of the most efficient approaches in the high-dimensional data processing. However, the existing graph-based clustering methods have high computational complexity and are difficult to obtain effective information when dealing with high-dimensional data. To solve this problem, we propose \underline{\textbf{E}}mbedded \underline{\textbf{F}}eature \underline{\textbf{S}}election on \underline{\textbf{G}}raph-based \underline{\textbf{M}}ulti-view \underline{\textbf{C}}lustering (EFSGMC) to enhance the clustering performance. Our model performs feature selection on the anchor graph with memory advantage to select a subset of the most relevant features. In addition, we use dispersed cluster assignment matrix to obtain cluster labels without post-processing. Finally, we applied the tensor Schatten $p$-norm as a tensor rank approximation function to obtain the complementary information between different views to ensure the similarity between the cluster assignment matrices. Experimental results on five real datasets demonstrate that our proposed method is superior to the state-of-the-art methods.

Embedded Feature Selection on Graph-Based Multi-View Clustering

Partial multi-view clustering is a challenging and practical research problem for data analysis in real-world applications, due to the potential data missing issue in different views. However, most existing methods have not fully explored the correlation information among various incomplete views. In addition, these existing clustering methods always ignore discovering discriminative features inside the data itself in this unsupervised task. To tackle these challenges, we propose Partial Multi-View Clustering via Self-Supervised \textbf{N}etwork (PVC-SSN) in this paper. 
Specifically, we employ contrastive learning to obtain a more discriminative and consistent subspace representation, which is guided by a self-supervised module. Self-supervised learning can exploit effective cluster information through the data itself to guide the learning process of clustering tasks. Thus, it can pull together embedding features from the same cluster and push apart these from different clusters. Extensive experiments on several benchmark datasets show that the proposed PVC-SCN method outperforms several state-of-the-art clustering methods.

Partial Multi-View Clustering via Self-Supervised Network

Graph-based multimedia data clustering has attracted much attention due to the impressive clustering performance for arbitrarily shaped multimedia data. However, existing graph-based clustering methods need post-processing to get labels for multimedia data with high computational complexity. Moreover, it is sub-optimal for label learning due to the fact that they exploit the complementary information embedded in data with different types pixel by pixel. To handle these problems, we present a novel label learning model with good interpretability for clustering. To be specific, our model decomposes anchor graph into the products of two matrices with orthogonal non-negative constraint to directly get soft label without any post-processing, which remarkably reduces the computational complexity. To well exploit the complementary information embedded in multimedia data, we introduce tensor Schatten p-norm regularization on the label tensor which is composed of soft labels of multimedia data. The solution can be obtained by iteratively optimizing four decoupled sub-problems, which can be solved more efficiently with good convergence. Experimental results on various datasets demonstrate the efficiency of our model.

Tensorized Label Learning on Anchor Graph

Although k-means clustering has been widely studied due to its simplicity, these methods still have the following two fatal drawbacks. Firstly, they need to initialize the cluster centers, which causes unstable clustering performance. Secondly, they have poor performance on non-Gaussian datasets. Inspired by the affinity matrix, we propose a novel multi-view k-means based on the adjacency matrix. It maps the affinity matrix to the distance matrix according to the principle that every sample has a small distance from the points in its neighborhood and a large distance from the points outside of the neighborhood. Moreover, this method well exploits the complementary information embedded in different views by minimizing the tensor Schatten p-norm regularizer on the third-order tensor which consists of cluster assignment matrices of different views. Additionally, this method avoids initializing cluster centroids to obtain stable performance. And there is no need to compute the means of clusters so that our model is not sensitive to outliers. Experiment on a toy dataset shows the excellent performance on non-Gaussian datasets. And other experiments on several benchmark datasets demonstrate the superiority of our proposed method.

Centerless Multi-View K-means Based on The Adjacency Matrix

Main Track - Machine Learning

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Quanxue Gao

4

SHORT BIO

Presentations

Embedded Feature Selection on Graph-Based Multi-View Clustering

Partial Multi-View Clustering via Self-Supervised Network

Tensorized Label Learning on Anchor Graph

Centerless Multi-View K-means Based on The Adjacency Matrix

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