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Adaptive data-driven dictionaries for sparse approximations provide superior performance compared to predefined dictionaries in applications involving representation and classification of data. In this paper, we propose a novel algorithm for learning global dictionaries particularly suited to the sparse representation of natural images. The proposed algorithm uses a hierarchical energy based learning...
The processing power of parallel coprocessors like the Graphics Processing Unit (GPU) is dramatically increasing. However, until now only a few approaches have been presented to utilize this kind of hardware for mesh clustering purposes. In this paper, we introduce a Multilevel clustering technique designed as a parallel algorithm and solely implemented on the GPU. Our formulation uses the spatial...
Given a graph G = (V, E) with real-valued edge weights, the problem of correlation k-clustering with pre-clustered items is to extend a k-clustering of distinguished vertices of G (pre-clustered items) to partition all the vertices into clusters so as to minimize the total absolute weight of cut positive edges and uncut negative edges. This problem for general graphs is APX-complete. A polynomial...
We consider the following correlation k-clustering problem: given a graph with real-valued edge weights (both positive and negative), extend a k-clustering of some vertices to partition all the vertices into clusters so as to maximize the total absolute weight of cut negative edges and uncut positive edges. This problem for general graphs is NP-complete for all fixed k ges 2. We present polynomial...
Clusters in protein interaction networks can potentially help identify functional relationships among proteins. The clustering problem can be modeled as a graph cut problem. Given an edge weighted graph the problem is to partition the vertices of the graph into k partitions of prescribed sizes such that the total weight of the edges within partitions are maximized. This problem is NP-complete for...
Clustering is the problem of finding relations in a data set in an supervised manner. These relations can be extracted using the density of a data set, where density of a data point is defined as the number of data points around it. To find the number of data points around another point, region queries are adopted. Region queries are the most expensive construct in density based algorithm, so it should...
A new filtering algorithm is presented for tracking multiple clusters of coordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithm maintains a discrete approximation of the filtering density of the clusters' state. The filter's tracking efficiency is enhanced by incorporating two stages into the basic Metropolis-Hastings sampling scheme: 1) Interaction. Improved...
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