Nearest Class Vector Classification for Large-Scale Learning Problems

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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In this paper, we describe a method for combined metric learning and classification, that is based on logistic discrimination for the determination of a low-dimensional feature space of increased discrimination power. An iterating optimization process is applied to this end, where the probability of correct classification rate is increased at each optimization step. Extensions of the method that allow richer class representations and non-linear feature space determination and classification are also described. The described optimization schemes are solved by following (stochastic or mini-batch) gradient descent optimization, which is well suited for large-scale learning problems.
Original languageEnglish
Title of host publication2015 IEEE Trustcom/BigDataSE/ISPA
Subtitle of host publicationProceedings of a meeting held 20-22 August 2015, Helsinki, Finland
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781467379526
ISBN (Print)9781467379533
Publication statusPublished - Jan 2016
EventIEEE International Conference on Big Data Science and Engineering (BigDataSE) - Helsinki, Finland
Duration: 20 Aug 201522 Aug 2015


ConferenceIEEE International Conference on Big Data Science and Engineering (BigDataSE)


  • Nearest Class Vector classification
  • Logistic Discrimination


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