Incremental Unsupervised Feature Selection for Dynamic Incomplete Multi-view Data

TL;DR

This paper introduces I$^2$MUFS, an incremental unsupervised feature selection method designed for incomplete multi-view streaming data, effectively handling missing views and reducing computational costs.

Contribution

The paper proposes a novel incremental feature selection approach that jointly considers multi-view data with missing views using an extended weighted non-negative matrix factorization model.

Findings

Outperforms state-of-the-art methods in clustering accuracy

Reduces computational cost significantly

Effectively handles incomplete multi-view streaming data

Abstract

Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume all of the views are complete. However, in real applications, the multi-view data are often incomplete, i.e., some views of instances are missing, which will result in the failure of these methods. Besides, while the data arrive in form of streams, these existing methods will suffer the issues of high storage cost and expensive computation time. To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view streaming data. By jointly considering the consistent and complementary information across different views, I$^2$MUFS embeds the unsupervised feature selection into an extended weighted non-negative matrix factorization model, which can learn a consensus clustering indicator matrix and fuse different latent feature matrices with adaptive view weights. Furthermore, we introduce the incremental leaning mechanisms to develop an alternative iterative algorithm, where the feature selection matrix is incrementally updated, rather than recomputing on the entire updated data from scratch. A series of experiments are conducted to verify the effectiveness of the proposed method by comparing with several state-of-the-art methods. The experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of the clustering metrics and the computational cost.