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Distributed machine learning is becoming increasingly popular for large scale data mining on large scale cluster. To mitigate the interference of straggler machines, recent distributed machine learning systems support flexible model consistency, which allows worker using a local stale model to compute model update without waiting for the newest model, while limiting the asynchronous step in a certain...
In order to process very large graphs, existing graph processing systems, such as Pregel and Giraph, usually partition and distribute the graph computation on large number of nodes (i.e., workers). However, due to the heterogeneity of computing clusters (e.g., nodes with various bandwidth or CPU resource), blindly increasing the number of workers for a job may even degrade the overall performance...
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection...
This paper presents a novel nonparametric clustering algorithm, called energy based evolving mean shift (EMS) clustering. It defines an energy function to characterize the compactness of the underlying data set and proves the clustering procedure converges. Through iterations, the data points collapse into well formed clusters and the associated energy approaches zero. Although as a general algorithm,...
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