1 research outputs found
Pairwise Constraint Propagation on Multi-View Data
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