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  1. Home
  3. Ib2mc: Information Bottleneck Inspired Balanced Multiview Clustering.
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  3. Ib2mc: Information Bottleneck Inspired Balanced Multiview Clustering.

Related Experiment Video

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

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

Published on: February 15, 2017

IB2MC: Information Bottleneck Inspired Balanced Multiview Clustering.

Shengzhao Guo, Jingyu Wang, Letian Zhang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 14, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Information Bottleneck Inspired Balanced Multiview Clustering (IB2MC) enhances clustering security and performance by balancing information compression and task-relevant retention. This novel framework overcomes limitations in unsupervised clustering and achieves superior results across diverse datasets.

    Related Experiment Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

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

    Published on: February 15, 2017

    Area of Science:

    • Machine Learning
    • Data Mining
    • Information Theory

    Background:

    • Unsupervised clustering methods face challenges with information bottleneck (IB) theory due to a lack of prior knowledge and poor clustering security.
    • Existing approaches often suffer from performance degradation caused by empty clusters resulting from excessive bias.

    Purpose of the Study:

    • To propose a scalable framework, Information Bottleneck Inspired Balanced Multiview Clustering (IB2MC), addressing clustering security, optimization synergy, and tuning simplicity.
    • To leverage unsupervised IB theory to enhance cluster distribution uncertainty and descriptor discriminability for improved label inference.

    Main Methods:

    • Developed IB2MC framework incorporating dynamic cosine graph learning for robust label transmission and seamless label extraction.
  • Implemented representation alignment with an independent constraint to improve cluster separability.
  • Utilized trace ratio for self-balanced clustering, minimizing the need for hyperparameter tuning.

    • Main Results:

    • The IB2MC method demonstrates linear time complexity concerning sample size, requiring no pre- or post-processing for joint optimization.
    • IB2MC achieved superior average performance, ranking first across twelve real-world datasets against seventeen state-of-the-art baseline methods.

    Conclusions:

    • IB2MC offers a secure, synergistic, and simple-to-tune framework for multiview clustering.
    • The proposed method effectively balances information compression and task-relevant information retention, outperforming existing approaches in unsupervised clustering tasks.