Exhaustive and Efficient Constraint Propagation: A Semi-Supervised
Learning Perspective and Its Applications
release_kaghhef4gfglhin64yobp2mlai
by
Zhiwu Lu, Horace H.S. Ip, Yuxin Peng
2011
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.
In text/plain
format
Archived Files and Locations
application/pdf 652.3 kB
file_5vnesjqtjbhdllqmvgoip5v4zi
|
archive.org (archive) |
1109.4684v1
access all versions, variants, and formats of this works (eg, pre-prints)