# Intrinsic Weight Learning Approach for Multi-view Clustering

## TL;DR

This paper introduces a novel intrinsic weight learning method for multi-view clustering, leveraging a re-weighted approach and a unified Laplacian rank constrained graph, demonstrating improved effectiveness and adaptability.

## Contribution

It proposes a new weight learning paradigm for multi-view clustering based on re-weighted approach and theoretical analysis, with a unified graph model for better view importance estimation.

## Key findings

- The proposed method effectively improves clustering performance.
- It is compatible with various existing clustering algorithms.
- Numerical experiments confirm its practicality and effectiveness.

## Abstract

Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. Generally, it is essential to measure the importance of each individual view, due to some noises, or inherent capacities in description. Many previous works model the view importance as weight, which is simple but effective empirically. In this paper, instead of following the traditional thoughts, we propose a new weight learning paradigm in context of multi-view clustering in virtue of the idea of re-weighted approach, and we theoretically analyze its working mechanism. Meanwhile, as a carefully achieved example, all of the views are connected by exploring a unified Laplacian rank constrained graph, which will be a representative method to compare with other weight learning approaches in experiments. Furthermore, the proposed weight learning strategy is much suitable for multi-view data, and it can be naturally integrated with many existing clustering learners. According to the numerical experiments, the proposed intrinsic weight learning approach is proved effective and practical to use in multi-view clustering.