Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

TL;DR

This paper introduces an efficient semi-supervised method for exhaustive pairwise constraint propagation using label propagation on k-nearest neighbor graphs, improving constrained spectral clustering and cross-modal multimedia retrieval.

Contribution

It proposes a novel quadratic-time semi-supervised approach for exhaustive pairwise constraint propagation applicable to multi-source data and multimedia retrieval.

Findings

Achieves efficient exhaustive constraint propagation with quadratic time complexity.

Enhances constrained spectral clustering accuracy.

Improves cross-modal multimedia retrieval performance.

Abstract

This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.