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

09/22/2011
by   Zhiwu Lu, et al.
0

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.

READ FULL TEXT

page 1

page 5

page 9

page 14

research
01/18/2015

Pairwise Constraint Propagation on Multi-View Data

This paper presents a graph-based learning approach to pairwise constrai...
research
02/19/2015

Pairwise Constraint Propagation: A Survey

As one of the most important types of (weaker) supervised information in...
research
04/17/2021

Fuzzy Discriminant Clustering with Fuzzy Pairwise Constraints

In semi-supervised fuzzy clustering, this paper extends the traditional ...
research
02/05/2015

Fast Constraint Propagation for Image Segmentation

This paper presents a novel selective constraint propagation method for ...
research
09/08/1999

The Rough Guide to Constraint Propagation

We provide here a simple, yet very general framework that allows us to e...
research
03/02/2021

Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints

Metric clustering is fundamental in areas ranging from Combinatorial Opt...
research
05/06/2018

Clustering With Pairwise Relationships: A Generative Approach

Semi-supervised learning (SSL) has become important in current data anal...

Please sign up or login with your details

Forgot password? Click here to reset