Low-dimensional data representation in data analysis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

Many Data Analysis tasks deal with data which are presented in high-dimensional spaces, and the ‘curse of dimensionality’ phenomena is often an obstacle to the use of many methods, including Neural Network methods, for solving these tasks. To avoid these phenomena, various Representation learning algorithms are used, as a first key step in solutions of these tasks, to transform the original high-dimensional data into their lower-dimensional representations so that as much information as possible is preserved about the original data required for the considered task. The above Representation learning problems are formulated as various Dimensionality Reduction problems (Sample Embedding, Data Manifold embedding, Data Manifold reconstruction and newly proposed Tangent Bundle Manifold Learning) motivated by various Data Analysis tasks. A new geometrically motivated algorithm that solves all the considered Dimensionality Reduction problems is presented.

Original languageEnglish
Title of host publicationArtificial Neural Networks in Pattern Recognition - 6th IAPR TC 3 InternationalWorkshop, ANNPR 2014, Proceedings
EditorsNeamat El Gayar, Friedhelm Schwenker, Ching Y. Suen
PublisherSpringer Verlag
Pages47-58
Number of pages12
ISBN (Electronic)9783319116556
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event6th IAPR TC3 International Workshop on Artificial Neural Networks for Pattern Recognition, ANNPR 2014 - Montreal, Canada
Duration: 6 Oct 20148 Oct 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8774
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th IAPR TC3 International Workshop on Artificial Neural Networks for Pattern Recognition, ANNPR 2014
Country/TerritoryCanada
CityMontreal
Period6/10/148/10/14

Keywords

  • Dimensionality reduction
  • Kernel methods
  • Machine learning
  • Manifold learning
  • Representation learning
  • Tangent bundle manifold learning
  • Tangent learning

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