Contrastive self-representation learning for data clustering

Wenhui Zhao; Quanxue Gao; Shikun Mei; Ming Yang

doi:10.1016/j.neunet.2023.08.050

ScienceGate Book Chapters

JOURNAL ARTICLE

Contrastive self-representation learning for data clustering

Wenhui Zhao Quanxue Gao Shikun Mei Ming Yang

Year: 2023 Journal: Neural Networks Vol: 167 Pages: 648-655 Publisher: Elsevier BV

DOI: 10.1016/j.neunet.2023.08.050

Get Full-Text PDF Get Analytical Report

Keywords:

Cluster analysis Similarity (geometry) Pattern recognition (psychology) Discriminative model Representation (politics) Computer science Artificial intelligence Feature learning Subspace topology Coefficient matrix Rank (graph theory) Noise (video) Mathematics Benchmark (surveying) Image (mathematics) Combinatorics

Metrics

Cited By

2.18

FWCI (Field Weighted Citation Impact)

Refs

0.86

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Remote-Sensing Image Classification

Physical Sciences → Engineering → Media Technology

Remote Sensing and Land Use

Physical Sciences → Earth and Planetary Sciences → Atmospheric Science

Contrastive self-representation learning for data clustering

Metrics

Citation History

Topics

Related Documents

Contrastive Self-supervised Representation Learning Using Synthetic Data

Contrastive and Non-Contrastive Strategies for Federated Self-Supervised Representation Learning and Deep Clustering

Clustering Enhanced Multiplex Graph Contrastive Representation Learning

Contrastive clustering with self-attention enhanced representation encoding

Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering