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  • An open source C++ implementation of multi-threaded Gaussian mixture models, k-means and expectation maximisation

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    Sanderson437145-Accepted.pdf (414.6Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Sanderson, Conrad
    Curtin, Ryan
    Griffith University Author(s)
    Sanderson, Conrad
    Year published
    2017
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    Abstract
    Modelling of multivariate densities is a core component in many signal processing, pattern recognition and machine learning applications. The modelling is often done via Gaussian mixture models (GMMs), which use computationally expensive and potentially unstable training algorithms. We provide an overview of a fast and robust implementation of GMMs in the C++ language, employing multi-threaded versions of the Expectation Maximisation (EM) and k-means training algorithms. Multi-threading is achieved through reformulation of the EM and k-means algorithms into a MapReduce-like framework. Furthermore, the implementation uses ...
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    Modelling of multivariate densities is a core component in many signal processing, pattern recognition and machine learning applications. The modelling is often done via Gaussian mixture models (GMMs), which use computationally expensive and potentially unstable training algorithms. We provide an overview of a fast and robust implementation of GMMs in the C++ language, employing multi-threaded versions of the Expectation Maximisation (EM) and k-means training algorithms. Multi-threading is achieved through reformulation of the EM and k-means algorithms into a MapReduce-like framework. Furthermore, the implementation uses several techniques to improve numerical stability and modelling accuracy. We demonstrate that the multi-threaded implementation achieves a speedup of an order of magnitude on a recent 16 core machine, and that it can achieve higher modelling accuracy than a previously well-established publically accessible implementation. The multi-threaded implementation is included as a user-friendly class in recent releases of the open source Armadillo C++ linear algebra library. The library is provided under the permissive Apache 2.0 license, allowing unencumbered use in commercial products.
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    Conference Title
    2017 11th International Conference on Signal Processing and Communication Systems (ICSPCS)
    DOI
    https://doi.org/10.1109/icspcs.2017.8270510
    Copyright Statement
    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Software engineering not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/395905
    Collection
    • Conference outputs

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