A fundamental step in the analysis of gene expression and other high-dimensional genomic data is the calculation of the similarity or distance between pairs of individual samples in a study. If one has collected N total samples and assayed the expression level of G genes on those samples, then an N x N similarity matrix can be formed that reflects the correlation or similarity of the samples wi...

Crowdsourcing utilizes human ability by distributing tasks to a large number of workers. It is especially suitable for solving data clustering problems because it provides a way to obtain a similarity measure between objects based on manual annotations, which capture the human perception of similarity among objects. This is in contrast to most clustering algorithms that face the challenge of fi...

Probabilistic mixture models are among the most important clustering methods. These models assume that the feature vectors of the samples can be described by a mixture of several components. Each of these components follows a distribution of a certain form. In recent years, there has been an increasing amount of interest and work in similarity-matrix-based methods. Rather than considering the f...

We study multi-level multivariate normal distribution with self similar compound symmetry covariance structure for k different levels of the multivariate data. Both maximum likelihood and unbiased estimates of the matrix parameters are obtained. The spectral decomposition of the new covariance structure are discussed and are demonstrated with a real dataset from medical studies.

Nowadays, a large number of new online businesses emerge rapidly. For these emerging businesses, existing recommendation models usually suffer from the data-sparsity. In this paper, we introduce a novel similarity measure, AmpSim (Augmented Meta Path-based Similarity) that takes both the linkage structures and the augmented link attributes into account. By traversing between heterogeneous netwo...

Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same cluster having high similarity and points in different clusters having low similarity. In this paper, we derive new cost functions for spectral clustering based on measures of error between a given partition and a solutio...

Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same cluster having high similarity and points in different clusters having low similarity. In this paper, we derive a new cost function for spectral clustering based on a measure of error between a given partition and a solut...