The Normalized Cut (NCut) objective function, widely used in data clustering and image segmentation, quantifies the cost of graph partitioning in a way that biases clusters or segments that are balanced towards having lower values than unbalanced partitionings. However, this bias is so strong that it avoids any singleton partitions, even when vertices are very weakly connected to the rest of th...

Node definition is a very important issue in human brain network analysis and functional connectivity studies. Typically, the atlases generated from meta-analysis, random criteria, and structural criteria are utilized as nodes in related applications. However, these atlases are not originally designed for such purposes and may not be suitable. In this study, we combined normalized cut (Ncut) an...

The robustness of complex networks under targeted attacks is deeply connected to the resilience of complex systems, i.e., the ability to make appropriate responses to the attacks. In this article, we investigated the state-of-the-art targeted node attack algorithms and demonstrate that they become very inefficient when the cost of the attack is taken into consideration. In this paper, we made e...

MOTIVATION The study of sequence space, and the deciphering of the structure of protein families and subfamilies, has up to now been required for work in comparative genomics and for the prediction of protein function. With the emergence of structural proteomics projects, it is becoming increasingly important to be able to select protein targets for structural studies that will appropriately co...

We present a novel approach for solving the text segmentation problem in natural scene images. The proposed algorithm uses the normalized graph cut(Ncut) as the measure for spectral clustering, and the weighted matrices used in evaluating the graph cuts are based on the gray levels of an image, rather than the commonly used image pixels. Thus, the proposed algorithm requires much smaller spatia...

We present a new view of clustering and segmentation by pairwise similarities. We interpret the similarities as edge ows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This view shows that spectral methods for clustering and segmentation have a probabilistic foundation. We prove that the Normalized Cut method arises naturally from our framewo...

This study proposes an unsupervised learning approach for the task of hand pose recognition. Considering the large variation in hand poses, classification using a decision tree seems highly suitable for this purpose. Various research works have used boosted decision trees and have shown encouraging results for pose recognition. This work also employs a boosted classifier tree learned in an unsu...