In this paper, we propose a simple but effective method for fast image segmentation. We re-examine the locality-preserving character of spectral clustering by constructing a graph over image regions with both global and local connections. Our novel approach to build graph connections relies on two key observations: 1) local region pairs that cooccur frequently will have a high probability to re...

Understanding the runtime behavior and dependencies between components in complex transaction-based enterprise systems enables the system administrators to identify performance bottlenecks, allocate resources, and detect failures. This paper introduces a novel method for extracting dependency information between system components at runtime by using delay injection on individual links and Fast ...

The task of knowledge graph construction presents a confounding challenge for statistical relational models. While the uncertainty of extractions from NLP tools and the ontological structure of knowledge are a perfect match for the strengths of statistical relational techniques, the vast and continually growing evidence from which knowledge graphs are constructed can make such models prohibitiv...

In practical machine learning systems, graph based data representation has been widely used in different learning paradigms, ranging from unsupervised clustering to supervised classification. Besides those applications with natural graph or network structure data, such as social network analysis and relational learning, many other applications often involve a critical step of the conversion fro...

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Construction of a nearest neighbor graph is often a necessary step in many machine learning applications. However, constructing such a graph is computationally expensive, especially when the data is of high dimensionality. In this work, we focus on the use of two tree structures, k-d trees and ball trees, to implement nearest neighbor graph construction. We present parallel implementations of n...