Semi-Supervised Clustering and Feature Discrimination with Instance-Level Constraints

Abstract

We propose a Semi-Supervised Clustering and Attribute Discrimination (S-SCAD) algorithm that performs fuzzy clustering and coarse feature weighting simultaneously. The supervision information in S-SCAD consists of a small set of constraints on which instances should or should not reside in the same cluster. The feature set is divided into logical subsets of features, and a degree of relevance is dynamically assigned to each subset based on its partial degree of dissimilarity. These weights have two advantages. First, they help in partitioning the data set into more meaningful clusters. Second, they can be used as part of a more complex learning system to enhance its learning behavior. We show that the partial supervision can guide the algorithm in learning the prototype parameters and the feature relevance weights, and thus, improve the final partition. The performance of the proposed algorithm is illustrated by using it to categorize a collection of color images. We use four feature subsets that encode color, structure, and texture information. The results are compared to other similar algorithms.