Bayesian regression selecting valuable subset from mixed bag training data

Takayuki Katsuki, Masato Inoue

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper addresses a problem in which we learn a regression model from sets of training data. Each of the sets has an only single label, and only one of the training data in the set reflects the label. This is particularly the case when the label is attached to a group of data, such as time-series data. The label is not attached to the point of the sequence but rather attached to particular time window of the sequence. As such, a small part of the time window likely reflects the label, whereas the other larger part of the time window likely does not reflect it. We design an algorithm for estimating which of the training data in each of the sets corresponds to the label, as well as for training the regression model on the basis of Bayesian modeling and posterior inference with variational Bayes. Our experimental results show that our approach perform better than baseline methods on an artificial dataset and on a real-world dataset.

    Original languageEnglish
    Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2580-2585
    Number of pages6
    ISBN (Electronic)9781509048472
    DOIs
    Publication statusPublished - 2017 Apr 13
    Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
    Duration: 2016 Dec 42016 Dec 8

    Other

    Other23rd International Conference on Pattern Recognition, ICPR 2016
    CountryMexico
    CityCancun
    Period16/12/416/12/8

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

    Fingerprint Dive into the research topics of 'Bayesian regression selecting valuable subset from mixed bag training data'. Together they form a unique fingerprint.

    Cite this