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Erschienen in:
Moving Object Detection and Tracking Based on Three-Frame Difference and Background Subtraction with Laplace Filter Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

An Empirical Study of Strategies Boosts Performance of Mutual Information Similarity

verfasst von : Ole Kristian Ekseth, Svein-Olav Hvasshovd

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

In the recent years, the application of mutual information based measures has received broad popularity. The mutual information MINE measure is asserted to be the best strategy for identification of relationships in challenging data sets. A major weakness of the MINE similarity metric concerns its high execution time. To address the performance issue numerous approaches are suggested both with respect to improvement of software implementations and with respect to the application of simplified heuristics. However, none of the approaches manage to address the high execution-time of MINE computation.
In this work, we address the latter issue. This paper presents a novel MINE implementation which manages a 530x+ performance increase when compared to established approaches. The novel high-performance approach is the result of a structural evaluation of 30+ different MINE software implementations, implementations which do not make use of simplified heuristics. Hence, the proposed strategy for computation of MINE mutual information is both accurate and fast. The novel mutual information MINE software is available at https://​bitbucket.​org/​oekseth/​mine-data-analysis/​downloads/​. To broaden the applicability the high-performance MINE metric is integrated into the hpLysis machine learning library (https://​bitbucket.​org/​oekseth/​hplysis-cluster-analysis-software).

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Vorheriges Kapitel On Ensemble Components Selection in Data Streams Scenario with Gradual Concept-Drift
Nächstes Kapitel Distributed Nonnegative Matrix Factorization with HALS Algorithm on Apache Spark
Fußnoten
1
To use low-level assembly instructions for hardware parallel computations (SSE) to reduce execution time.
 
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Metadaten
Titel
An Empirical Study of Strategies Boosts Performance of Mutual Information Similarity
verfasst von
Ole Kristian Ekseth
Svein-Olav Hvasshovd
Copyright-Jahr
2018
Buch
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-91261-5

Electronic ISBN: 978-3-319-91262-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-91262-2_29