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Updated: Jun 3, 2026

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

Maximum margin multiple instance clustering with applications to image and text clustering.

Dan Zhang¹, Fei Wang, Luo Si

¹Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA. danzhang2008@gmail.com

IEEE Transactions on Neural Networks

|March 31, 2011

Summary

This summary is machine-generated.

This study introduces Maximum Margin Multiple Instance Clustering (M(3)IC), a novel framework for clustering problems where data is organized in bags of instances. The proposed method offers an efficient and effective solution for multiple instance clustering tasks.

Related Experiment Videos

Last Updated: Jun 3, 2026

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

Area of Science:

Machine Learning
Data Mining
Pattern Recognition

Background:

Multiple instance learning (MIL) problems involve data structured as bags of instances.
Existing MIL research primarily focuses on classification and regression, with limited work on clustering.
Multiple instance clustering (MIC) presents unique challenges due to the hierarchical data structure.

Purpose of the Study:

To propose a novel framework for multiple instance clustering (MIC).
To address the computational challenges in solving the optimization problem for MIC.
To provide an efficient and effective method for MIC tasks.

Main Methods:

Formulation of the Maximum Margin Multiple Instance Clustering (M(3)IC) framework.
Relaxation of the M(3)IC optimization problem for efficient solving.
Combination of the constrained concave-convex procedure and the cutting plane method for optimization.

Main Results:

Development of an efficient optimization solution for M(3)IC.
Demonstration of the proposed method's effectiveness and efficiency through extensive empirical results.
Comparison highlighting the advantages of M(3)IC over existing MIC research.

Conclusions:

The proposed M(3)IC framework provides a viable and efficient approach to multiple instance clustering.
The combination of optimization techniques enables practical application of the M(3)IC method.
Empirical evidence supports the superiority of M(3)IC in terms of both effectiveness and efficiency.