Unsupervised Feature Ranking and Selection

Dimensionality reduction is an important issue for efficient handling of large data sets. Feature selection is effective in dimensionality reduction. Many supervised feature selection methods exist. Little work has been done for unsupervised feature ranking and selection where class information is not available. In this chapter, we are concerned with the problem of determining and choosing the important original features for unsupervised data. Our method is based on the observation that removing an irrelevant feature may not change the underlying concept of the data, but not so otherwise. We propose an entropy measure for ranking features, and conduct experiments to verify that the proposed method is able to find important features. For verification purpose, we compare it with a feature ranking method (Relief) that requires class information, and test the reduced data for tasks of clustering and model construction. This work can also be extended to dimensionality reduction for data with continuous class.