关键词: 不完整多视图, 度量学习, 表示学习, 差异性, 一致性

Abstract: Abstract: In practical applications, multi-view metric learning has become an effective method for handling multi-view data. However, the incompleteness of multi-view data poses a significant challenge for multi-view metric learning. Although some methods have attempted to address the issue of incomplete multi-view data, they still have the following shortcomings: i) Most methods rely on k-nearest neighbors of existing samples to fill in missing data, which can overlook the unique characteristics of samples or views; ii) They only utilize existing sample representations to calculate neighbors, which cannot fully express the neighbor relationships between samples. To address these issues, a Dual Completion-based Incomplete Multi-View Metric Learning algorithm (DIMVML) was proposed. The algorithm first extracts latent features of each view using a deep autoencoder, then fills in missing samples by combining distribution information of the samples and differences between views; secondly, it fuses the results based on the quality of the completed samples to obtain higher-quality completion results; finally, it optimizes intra-view and inter-view relationships through a loss function. In the experimental analysis, six classic datasets were selected for testing and analysis. The experimental results further demonstrate the effectiveness of the proposed new algorithm.

Key words: incomplete multi-view, metric learning, representation learning, difference, consistency