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Discriminative k-metrics

Published: 14 June 2009 Publication History

Abstract

The k q-flats algorithm is a generalization of the popular k-means algorithm where q dimensional best fit affine sets replace centroids as the cluster prototypes. In this work, a modification of the k q-flats framework for pattern classification is introduced. The basic idea is to replace the original reconstruction only energy, which is optimized to obtain the k affine spaces, by a new energy that incorporates discriminative terms. This way, the actual classification task is introduced as part of the design and optimization. The presentation of the proposed framework is complemented with experimental results, showing that the method is computationally very efficient and gives excellent results on standard supervised learning benchmarks.

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cover image ACM Other conferences
ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning
June 2009
1331 pages
ISBN:9781605585161
DOI:10.1145/1553374

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  • NSF
  • Microsoft Research: Microsoft Research
  • MITACS

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2009

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Overall Acceptance Rate 140 of 548 submissions, 26%

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  • (2015)Discriminative Collaborative Representation for ClassificationComputer Vision -- ACCV 201410.1007/978-3-319-16817-3_14(205-221)Online publication date: 17-Apr-2015
  • (2012)Multi-scale geometric methods for data sets II: Geometric Multi-Resolution AnalysisApplied and Computational Harmonic Analysis10.1016/j.acha.2011.08.00132:3(435-462)Online publication date: May-2012
  • (2012)Multi-Resolution Geometric Analysis for Data in High DimensionsExcursions in Harmonic Analysis, Volume 110.1007/978-0-8176-8376-4_13(259-285)Online publication date: 20-Nov-2012
  • (2011)Some Recent Advances in Multiscale Geometric Analysis of Point CloudsWavelets and Multiscale Analysis10.1007/978-0-8176-8095-4_10(199-225)Online publication date: 23-Feb-2011
  • (2010)Dictionary learning and sparse coding for unsupervised clustering2010 IEEE International Conference on Acoustics, Speech and Signal Processing10.1109/ICASSP.2010.5494985(2042-2045)Online publication date: Mar-2010
  • (2010)Classification and clustering via dictionary learning with structured incoherence and shared features2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2010.5539964(3501-3508)Online publication date: Jun-2010

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