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Uncorrelated multilinear principal component analysis through successive variance maximization

Published: 05 July 2008 Publication History

Abstract

Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their most important applications. This paper extends the classical principal component analysis (PCA) to its multilinear version by proposing a novel unsupervised dimensionality reduction algorithm for tensorial data, named as uncorrelated multilinear PCA (UMPCA). UMPCA seeks a tensor-to-vector projection that captures most of the variation in the original tensorial input while producing uncorrelated features through successive variance maximization. We evaluate the UMPCA on a second-order tensorial problem, face recognition, and the experimental results show its superiority, especially in low-dimensional spaces, through the comparison with three other PCA-based algorithms.

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cover image ACM Other conferences
ICML '08: Proceedings of the 25th international conference on Machine learning
July 2008
1310 pages
ISBN:9781605582054
DOI:10.1145/1390156
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Pascal
  • University of Helsinki
  • Xerox
  • Federation of Finnish Learned Societies
  • Google Inc.
  • NSF
  • Machine Learning Journal/Springer
  • Microsoft Research: Microsoft Research
  • Intel: Intel
  • Yahoo!
  • Helsinki Institute for Information Technology
  • IBM: IBM

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Association for Computing Machinery

New York, NY, United States

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Published: 05 July 2008

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  • Intel
  • IBM

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Overall Acceptance Rate 140 of 548 submissions, 26%

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  • (2024)Icing detection and prediction for wind turbines using multivariate sensor data and machine learningRenewable Energy10.1016/j.renene.2024.120879231(120879)Online publication date: Sep-2024
  • (2018)Spatially weighted PCA for monitoring video image data with application to additive manufacturingJournal of Quality Technology10.1080/00224065.2018.150756350:4(391-417)Online publication date: 31-Oct-2018
  • (2018)Tensor Discriminant Analysis with Partial LabelProcedia Computer Science10.1016/j.procs.2018.04.225131:C(416-424)Online publication date: 1-May-2018
  • (2018)Cluster Indexing and GR Encoding with Similarity Measure for CBIR ApplicationsTextual and Visual Information Retrieval using Query Refinement and Pattern Analysis10.1007/978-981-13-2559-5_5(93-122)Online publication date: 30-Sep-2018
  • (2016)Indexing and encoding based image feature representation with bin overlapped similarity measure for CBIR applicationsJournal of Visual Communication and Image Representation10.1016/j.jvcir.2016.01.00336:C(40-55)Online publication date: 1-Apr-2016
  • (2015)Image-Based Process Monitoring Using Low-Rank Tensor DecompositionIEEE Transactions on Automation Science and Engineering10.1109/TASE.2014.232702912:1(216-227)Online publication date: Jan-2015
  • (2014)An MPCA/LDA Based Dimensionality Reduction Algorithm for Face RecognitionMathematical Problems in Engineering10.1155/2014/3932652014:1Online publication date: 31-Aug-2014
  • (2014)Semisupervised Sparse Multilinear Discriminant AnalysisJournal of Computer Science and Technology10.1007/s11390-014-1490-129:6(1058-1071)Online publication date: 17-Nov-2014
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