Unsupervised Multi-view Learning: A Deep Dive into Feature and Instance Selection

The research focuses on unsupervised learning techniques for multi-view data, addressing the challenge of feature and instance selection. The cross-view imputation method presents a potentially novel approach to handle missing data and improve model performance within this framework.

• •Addresses unsupervised learning problems in multi-view data.
• •Proposes a novel method involving cross-view imputation.
• •Focuses on feature and instance co-selection.