Pairwise Constraint Propagation on Multi-View Data

TL;DR

This paper introduces a novel graph-based semi-supervised learning method for inter-view pairwise constraint propagation in multi-view data, addressing a previously underexplored challenge and demonstrating promising results in cross-view retrieval.

Contribution

It is the first to propose an efficient semi-supervised approach for inter-view constraint propagation using graph-based label propagation techniques.

Findings

Effective inter-view constraint propagation in multi-view data.

Improved cross-view retrieval performance.

Two new constrained graph construction methods.

Abstract

This paper presents a graph-based learning approach to pairwise constraint propagation on multi-view data. Although pairwise constraint propagation has been studied extensively, pairwise constraints are usually defined over pairs of data points from a single view, i.e., only intra-view constraint propagation is considered for multi-view tasks. In fact, very little attention has been paid to inter-view constraint propagation, which is more challenging since pairwise constraints are now defined over pairs of data points from different views. In this paper, we propose to decompose the challenging inter-view constraint propagation problem into semi-supervised learning subproblems so that they can be efficiently solved based on graph-based label propagation. To the best of our knowledge, this is the first attempt to give an efficient solution to inter-view constraint propagation from a semi-supervised learning viewpoint. Moreover, since graph-based label propagation has been adopted for basic optimization, we develop two constrained graph construction methods for interview constraint propagation, which only differ in how the intra-view pairwise constraints are exploited. The experimental results in cross-view retrieval have shown the promising performance of our inter-view constraint propagation.