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Related Experiment Video

Updated: Sep 29, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

A Survey on Multi-View Clustering.

Guoqing Chao¹, Shiliang Sun², Jinbo Bi³

¹School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, PR China.

IEEE Transactions on Artificial Intelligence

|March 21, 2022

Summary

This summary is machine-generated.

This study introduces a new classification system for multi-view clustering (MVC) algorithms, categorizing them into generative and discriminative approaches. It also analyzes current MVC methods and suggests future research directions.

Keywords:

Multi-view learning canonical correlation analysis clustering data mining k-means machine learning nonnegative matrix factorization spectral clustering subspace clustering survey

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Area of Science:

Machine Learning
Data Science
Artificial Intelligence

Background:

Clustering algorithms group similar data points.
Multi-view data, from different angles or modalities, requires specialized clustering techniques.
Existing multi-view clustering (MVC) methods lack a comprehensive survey and unified taxonomy.

Purpose of the Study:

To propose a novel taxonomy for multi-view clustering (MVC) approaches.
To summarize and analyze the current progress in MVC.
To provide insights into future research directions for MVC.

Main Methods:

Categorized MVC methods into generative and discriminative classes.
Further classified discriminative methods based on view integration strategies (e.g., Common Eigenvector Matrix, Common Coefficient Matrix).
Related MVC to other machine learning topics and evaluated representative algorithms on benchmark datasets.

Main Results:

Developed a novel taxonomy for MVC approaches.
Empirically evaluated representative MVC algorithms across different categories.
Identified several open problems for future MVC research.

Conclusions:

The proposed taxonomy provides a structured overview of MVC methods.
Empirical evaluations offer insights into algorithm performance.
Further research is needed to address identified open problems in MVC.