Joint Learning of Unsupervised Multi-view Feature and Instance Co-selection with Cross-view Imputation

TL;DR

JUICE is a unified framework that simultaneously performs missing data imputation and feature-instance co-selection in incomplete multi-view datasets, leveraging inter-sample relationships and cross-view information for improved selection.

Contribution

The paper introduces JUICE, a novel joint learning method that integrates imputation with co-selection, exploiting inter-sample and cross-view relationships for better performance.

Findings

JUICE outperforms existing methods in experiments.

Joint learning improves imputation accuracy.

Enhanced feature and instance selection quality.

Abstract

Feature and instance co-selection, which aims to reduce both feature dimensionality and sample size by identifying the most informative features and instances, has attracted considerable attention in recent years. However, when dealing with unlabeled incomplete multi-view data, where some samples are missing in certain views, existing methods typically first impute the missing data and then concatenate all views into a single dataset for subsequent co-selection. Such a strategy treats co-selection and missing data imputation as two independent processes, overlooking potential interactions between them. The inter-sample relationships gleaned from co-selection can aid imputation, which in turn enhances co-selection performance. Additionally, simply merging multi-view data fails to capture the complementary information among views, ultimately limiting co-selection effectiveness. To address these issues, we propose a novel co-selection method, termed Joint learning of Unsupervised multI-view feature and instance Co-selection with cross-viEw imputation (JUICE). JUICE first reconstructs incomplete multi-view data using available observations, bringing missing data recovery and feature and instance co-selection together in a unified framework. Then, JUICE leverages cross-view neighborhood information to learn inter-sample relationships and further refine the imputation of missing values during reconstruction. This enables the selection of more representative features and instances. Extensive experiments demonstrate that JUICE outperforms state-of-the-art methods.