Search Results for author: Quanxue Gao

Found 6 papers, 0 papers with code

Rethinking k-means from manifold learning perspective

no code implementations12 May 2023 Quanxue Gao, Qianqian Wang, Han Lu, Wei Xia, Xinbo Gao

Although numerous clustering algorithms have been developed, many existing methods still leverage k-means technique to detect clusters of data points.

Clustering

Multi-View Clustering via Semi-non-negative Tensor Factorization

no code implementations29 Mar 2023 Jing Li, Quanxue Gao, Qianqian Wang, Wei Xia, Xinbo Gao

Multi-view clustering (MVC) based on non-negative matrix factorization (NMF) and its variants have received a huge amount of attention in recent years due to their advantages in clustering interpretability.

Clustering

Effective and Efficient Graph Learning for Multi-view Clustering

no code implementations15 Aug 2021 Quanxue Gao, Wei Xia, Xinbo Gao, Xiangdong Zhang, Qin Li, DaCheng Tao

Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the following drawbacks.

Clustering Graph Learning

Multiple Graph Learning for Scalable Multi-view Clustering

no code implementations29 Jun 2021 Tianyu Jiang, Quanxue Gao, Xinbo Gao

Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm regularizer.

Clustering graph construction +1

Generative Partial Multi-View Clustering

no code implementations29 Mar 2020 Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, Yun Fu

Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis.

Clustering Imputation

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