Abstract
Feature selection is an efficient dimensionality reduction technique for artificial intelligence and machine learning. Many feature selection methods
learn the data structure to select the most discriminative features for distinguishing different classes.
However, the data is sometimes distributed in multiple parties and sharing the original data is difficult
due to the privacy requirement. As a result, the data
in one party may be lack of useful information to
learn the most discriminative features. In this paper, we propose a novel distributed method which
allows collaborative feature selection for multiple
parties without revealing their original data. In the
proposed method, each party finds the intermediate
representations from the original data, and shares
the intermediate representations for collaborative
feature selection. Based on the shared intermediate representations, the original data from multiple parties are transformed to the same low dimensional space. The feature ranking of the original
data is learned by imposing row sparsity on the
transformation matrix simultaneously. Experimental results on real-world datasets demonstrate the
effectiveness of the proposed method