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Metric-space Positioning Systems (MPS) for Machine Learning

Published: 02 October 2016 Publication History

Abstract

Many machine learning techniques such as k-nearest neighbors (KNNs) and support vector machines (SVMs) require examples to be mapped to numerical feature vectors. Principal coordinate analysis (PCoA) accomplishes this by mapping a set of n examples to n points in Rn-1. However, learning from these high-dimensional vectors may require an astronomically large number of examples. Here we present an intuitive, novel method for uniquely representing sequences of symbolic features with the fewest dimensions via "multi-lateration" (i.e. the selection of a minimal feature set that uniquely distinguishes all examples under a reference metric). We show that the problem of determining a minimal multilateration set is NP-complete in general, and present a randomized algorithm for finding close to optimal subsets. As proof of concept, we apply multilateration to learn 12-mers centered at intron-exon boundaries using human annotated examples. We compare results using features derived in several different ways and an array of machine learning techniques, some of which can handle symbolic features directly and some of which cannot. Our experiments indicate that multilateration improves performance of non-symbolic classification techniques without significantly altering performance using other techniques.
  1. Metric-space Positioning Systems (MPS) for Machine Learning

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    cover image ACM Conferences
    BCB '16: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
    October 2016
    675 pages
    ISBN:9781450342254
    DOI:10.1145/2975167
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

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    Published: 02 October 2016

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    Author Tags

    1. Dimension Reduction
    2. Multilateration
    3. NP-Complete
    4. Sequence Features

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