Minimum spanning trees (MSTs) have long been used in data mining, pattern recognition
and machine learning. However, it is difficult to apply traditional MST algorithms to a large …

The aim of clustering ensemble is to combine multiple base partitions into a robust, stable
and accurate partition. One of the key problems of clustering ensemble is how to exploit the …

This paper presents a novel active learning surrogate model for estimating low failure
probability in the reliability analysis of complex structures based on a radial basis function …

Collaborative filtering as a classical method of information retrieval has been widely used in
helping people to deal with information overload. In this paper, we introduce the concept of …

It is crucial to evaluate the quality of clustering results in cluster analysis. Although many
cluster validity indices (CVIs) have been proposed in the literature, they have some …

In this paper, we study bottleneck identification in networks via extracting minimax paths.
Many real-world networks have stochastic weights for which full knowledge is not available …

This paper describes a technique to (1) learn the representation of a pathological motion
pattern from a given population, and (2) compare individuals to this population. Our …

Semi-supervised learning (SSL) is attractive for labeling a large amount of data. Motivated
from cluster assumption, we present a path-based SSL framework for efficient large-scale …

We propose unsupervised representation learning and feature extraction from dendrograms.
The commonly used Minimax distance measures correspond to building a dendrogram with …

We investigate the use of Minimax distances to extract in a nonparametric way the features
that capture the unknown underlying patterns and structures in the data. We develop a …