Zhiwen Yu

Graph-based consensus clustering for class discovery from gene expression data

MOTIVATION Consensus clustering, also known as cluster ensemble, is one of the important techniques for microarray data analysis, and is particularly useful for class discovery from microarray data. Compared with traditional clustering algorithms, consensus clustering approaches have the ability to integrate multiple partitions from different cluster solutions to improve the robustness, stability, scalability and parallelization of the clustering algorithms. By consensus clustering, one can discover the underlying classes of the samples in gene expression data. RESULTS In addition to exploring a graph-based consensus clustering (GCC) algorithm to estimate the underlying classes of the samples in microarray data, we also design a new validation index to determine the number of classes in microarray data. To our knowledge, this is the first time in which GCC is applied to class discovery for microarray data. Given a pre specified maximum number of classes (denoted as K(max) in this article), our algorithm can discover the true number of classes for the samples in microarray data according to a new cluster validation index called the Modified Rand Index. Experiments on gene expression data indicate that our new algorithm can (i) outperform most of the existing algorithms, (ii) identify the number of classes correctly in real cancer datasets, and (iii) discover the classes of samples with biological meaning. AVAILABILITY Matlab source code for the GCC algorithm is available upon request from Zhiwen Yu.

Incremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering

Traditional cluster ensemble approaches have three limitations: (

A Bayesian Model for Crowd Escape Behavior Detection

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Semi-supervised classification based on random subspace dimensionality reduction

) They do not make use of prior knowledge of the datasets given by experts. (

SC³: Triple Spectral Clustering-Based Consensus Clustering Framework for Class Discovery from Cancer Gene Expression Profiles

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Class Discovery From Gene Expression Data Based on Perturbation and Cluster Ensemble

) Most of the conventional cluster ensemble methods cannot obtain satisfactory results when handling high dimensional data. (

A Rule Based Technique for Extraction of Visual Attention Regions Based on Real-Time Clustering

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Hybrid Adaptive Classifier Ensemble

) All the ensemble members are considered, even the ones without positive contributions. In order to address the limitations of conventional cluster ensemble approaches, we first propose an incremental semi-supervised clustering ensemble framework (ISSCE) which makes use of the advantage of the random subspace technique, the constraint propagation approach, the proposed incremental ensemble member selection process, and the normalized cut algorithm to perform high dimensional data clustering. The random subspace technique is effective for handling high dimensional data, while the constraint propagation approach is useful for incorporating prior knowledge. The incremental ensemble member selection process is newly designed to judiciously remove redundant ensemble members based on a newly proposed local cost function and a global cost function, and the normalized cut algorithm is adopted to serve as the consensus function for providing more stable, robust, and accurate results. Then, a measure is proposed to quantify the similarity between two sets of attributes, and is used for computing the local cost function in ISSCE. Next, we analyze the time complexity of ISSCE theoretically. Finally, a set of nonparametric tests are adopted to compare multiple semi-supervised clustering ensemble approaches over different datasets. The experiments on 18 real-world datasets, which include six UCI datasets and 12 cancer gene expression profiles, confirm that ISSCE works well on datasets with very high dimensionality, and outperforms the state-of-the-art semi-supervised clustering ensemble approaches.

A modified support vector machine and its application to image segmentation

People naturally escape from a place when unexpected events happen. Based on this observation, efficient detection of crowd escape behavior in surveillance videos is a promising way to perform timely detection of anomalous situations. In this paper, we propose a Bayesian framework for escape detection by directly modeling crowd motion in both the presence and absence of escape events. Specifically, we introduce the concepts of potential destinations and divergent centers to characterize crowd motion in the above two cases respectively, and construct the corresponding class-conditional probability density functions of optical flow. Escape detection is finally performed based on the proposed Bayesian framework. Although only data associated with nonescape behavior are included in the training set, the density functions associated with the case of escape can also be adaptively updated using observed data. In addition, the identified divergent centers indicate possible locations at which the unexpected events occur. The performance of our proposed method is validated in a number of experiments on crowd escape detection in various scenarios.

Knowledge Based Cluster Ensemble for Cancer Discovery From Biomolecular Data

Graph structure is vital to graph based semi-supervised learning. However, the problem of constructing a graph that reflects the underlying data distribution has been seldom investigated in semi-supervised learning, especially for high dimensional data. In this paper, we focus on graph construction for semi-supervised learning and propose a novel method called Semi-Supervised Classification based on Random Subspace Dimensionality Reduction, SSC-RSDR in short. Different from traditional methods that perform graph-based dimensionality reduction and classification in the original space, SSC-RSDR performs these tasks in subspaces. More specifically, SSC-RSDR generates several random subspaces of the original space and applies graph-based semi-supervised dimensionality reduction in these random subspaces. It then constructs graphs in these processed random subspaces and trains semi-supervised classifiers on the graphs. Finally, it combines the resulting base classifiers into an ensemble classifier. Experimental results on face recognition tasks demonstrate that SSC-RSDR not only has superior recognition performance with respect to competitive methods, but also is robust against a wide range of values of input parameters.