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Cluster ensembles are approaches to combine different clustering results to obtain a robust consensus partitioning. However, many cluster ensemble methods suffer from the problem of scalability since the extensive cost of calculating co-association matrix, which makes it hard to perform cluster ensemble on large scale datasets. In this paper, we proposed a scalable co-association cluster ensemble...
For problems of image or video segmentation, where clusters have a complex structure, a leading method is spectral clustering. It works by encoding the similarity between pairs of points into an affinity matrix and applying k-means in its low-order eigenspace, where the clustering structure is enhanced. When the number of points is large, an approximation is necessary to limit the runtime even if...
Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are not linearly separable. Kernel k-Means is...
We aim to understand and characterize embeddings of datasets with small anomalous clusters using the Laplacian Eigenmaps algorithm. To do this, we characterize the order in which eigenvectors of a disjoint graph Laplacian emerge and the support of those eigenvectors. We then extend this characterization to weakly connected graphs with clusters of differing sizes, utilizing the theory of invariant...
In spectral clustering approaches is of great importance how is built the graph representation over a data set, being reflected in the achieved clustering performance. In this work is introduced a methodology to build a graph representation of a given data, based on compactly supported radial basis functions which enables to highlight relevant pair-wise sample relationships. To tune such functions,...
We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost....
We investigate the use of graphics processing units (GPUs) in accelerating Page Rank computation. We first introduce a compact web graph representation which requires much less memory allocation than a well-known compressed sparse row format. The web graph is then simply partition into smaller chunks to fit the GPUs' device memory. We propose a fast Page Rank algorithm to run on the GPU cluster. The...
The massively parallel computing using graphical processing unit (GPU), which based on tens of thousands of parallel threats within hundreds of GPU's streaming processors, has gained broad popularity and attracted researchers in a wide range of application areas from finance, computer aided engineering, computational fluid dynamics, game physics, numerics, science, medical imaging, life science, and...
Markov clustering is becoming a key algorithm with in bioinformatics for determining clusters in networks. For instance, clustering protein interaction networks is helping find genes implicated in diseases such as cancer. However, with fast sequencing and other technologies generating vast amounts of data on biological networks, performance and scalability issues are becoming a critical limiting factorin...
Fusion of multiple information sources can yield significant benefits to accomplishing certain learning tasks. This paper exploits the sparse representation of signals for the problem of data clustering. The method is built within the framework of spectral clustering algorithms, which convexly combines a real graph constructed from the given physical features with a virtual graph constructed from...
We propose a randomized algorithm of spectral clustering and apply it to appearance-based image/video segmentation. Spectral clustering is a kernel-based method of grouping data on separate nonlinear manifolds. However, its high computational expensive restricts the applications. Our algorithm exploits random projection and subsampling techniques for reducing dimensionality and cardinality of data...
Clustering performance can often be greatly improved by leveraging side information. In this paper, we consider constrained clustering with pairwise constraints, which specify some pairs of objects from the same cluster or not. The main idea is to design a kernel to respect both the proximity structure of the data and the given pairwise constraints. We propose a spectral kernel learning framework...
We identify the challenges that are special to parallel sparse matrix-matrix multiplication (PSpGEMM). We show that sparse algorithms are not as scalable as their dense counterparts, because in general, there are not enough non-trivial arithmetic operations to hide the communication costs as well as the sparsity overheads. We analyze the scalability of 1D and 2D algorithms for PSpGEMM. While the 1D...
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