Recently, the paper “Joint Learning of Unsupervised Multi-view Feature and Instance Co-selection with Cross-view Imputation,” co-authored by Ph.D. student Yuxin Cai, Professor Yanyong Huang and Professor Jinyuan Chang from the team, with Professor Dongjie Wang of Northeast Normal University, Professor Tianrui Li of Southwest Jiaotong University, and Professor Xiaoyi Jiang of the University of Münster, was accepted for publication in IEEE Transactions on Knowledge and Data Engineering, a leading international journal in the field of data mining.
The paper proposes a joint feature and instance selection method for incomplete multi-view data, integrating missing data imputation, feature selection, and instance selection into a unified framework for collaborative optimization. By reducing feature and sample redundancy while enhancing the discriminative and representative capabilities of data representations, the proposed method effectively achieves data reduction and improves the performance of downstream tasks.
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.
Author Introduction
Yuxin Cai is a Ph.D. student in Economics and Big Data at Southwestern University of Finance and Economics. Her research primarily focuses on data mining.
Yanyong Huang is a Professor and Doctoral Supervisor at the Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics. He primarily engaged in research related to Data-centric AI, multimodal learning, and incremental learning.
Jinyuan Chang is a Guanghua Chair Professor at Southwestern University of Finance and Economics, Executive Dean of the Institute for Interdisciplinary Innovation in Statistics, and Executive Director of the Joint Laboratory of Data Science and Business Intelligence. His main research interests include complex data analysis.
Dongjie Wang is a Professor at Northeast Normal University. His main research interests include Data-centric AI, causal graph learning, and spatiotemporal data mining.
Tianrui Li is the Dean of the School of Computer and Artificial Intelligence at Southwest Jiaotong University and the Director of the Sichuan Institute of Industrial Software Technology. His main research interests include artificial intelligence, data mining and knowledge discovery, cloud computing and big data, granular computing, and rough sets.
Xiaoyi Jiang is a Professor in the Department of Mathematics and Computer Science at the University of Münster, a Fellow of the International Association for Pattern Recognition (IAPR), a Fellow of the Asian Association of Artificial Intelligence (AAAI), and the Chair of the Association of Chinese Professors in Germany. His main research interests include computer vision, pattern recognition, machine learning, and biomedical image analysis.
