Study Features via Exploring Distribution Structure
Chunxu Cao Qiang Zhang
In this paper, we present a novel framework for data redundancy measurement
based on probabilistic modeling of datasets, and a new criterion for redundancy
detection that is resilient to noise. We also develop new methods for data
redundancy reduction using both deterministic and stochastic optimization
techniques. Our framework is flexible and can handle different types of
features, and our experiments on benchmark datasets demonstrate the
effectiveness of our methods. We provide a new perspective on feature
selection, and propose effective and robust approaches for both supervised and
unsupervised learning problems.