We propose a novel framework for constrained spectral
clustering with pairwise constraints which specify whether
two objects belong to the same cluster or not. Unlike previous
m...
Zhenguo Li (The Chinese University of Hong Kong), ...
High availability for both data and applications is rapidly becoming a business requirement. While database systems support recovery, providing high database availability, applica...
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of pr...
Graph-based methods for semi-supervised learning have recently been shown to be promising for combining labeled and unlabeled data in classification problems. However, inference f...