Multi-view Construction for Clustering Based on Feature set Partitioning

Xiaojing Chang, Yan Yang, Hongiun Wang

Abstract: Multi-view learning take full advantage of multiple perspectives of data to improve learning performance. However, there are a lot of single-view data with high dimensions and low quantity in real life. Although traditional feature reduction methods can achieve the purpose of dimensionality reduction, a part of information will be lost. In order to make full use of the existing information of data and play advantages of multi view learning, a multi-view construction method based on feature set partitioning is proposed. This method firstly constructs the initial view by using a series of feature selection algorithms and then presents a multi-view evaluation method that is used to look for the most suitable view for a single feature to insert until all irrelevant features in original feature set are discarded while achieving the optimal partitioning, thereby multiple views are constructed. Experiments show that this method builds high quality multiple views to further improve clustering performance.