Discriminative dimensionality reduction for multi-dimensional sequences

Bing Su, Xiaoqing Ding, Life Fellow, Hao Wang, Ying Wu

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Since the observables at particular time instants in a temporal sequence exhibit dependencies, they are not independent samples. Thus, it is not plausible to apply i.i.d. assumption-based dimensionality reduction methods to sequence data. This paper presents a novel supervised dimensionality reduction approach for sequence data, called Linear Sequence Discriminant Analysis (LSDA). It learns a linear discriminative projection of the feature vectors in sequences to a lower-dimensional subspace by maximizing the separability of the sequence classes such that the entire sequences are holistically discriminated. The sequence class separability is constructed based on the sequence statistics, and the use of different statistics produces different LSDA methods. This paper presents and compares two novel LSDA methods, namely M-LSDA and D-LSDA. M-LSDA extracts model-based statistics by exploiting the dynamical structure of the sequence classes, and D-LSDA extracts the distance-based statistics by computing the pairwise similarity of samples from the same sequence class. Extensive experiments on several different tasks have demonstrated the effectiveness and the general applicability of the proposed methods.

Original languageEnglish (US)
Pages (from-to)77-91
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume40
Issue number1
DOIs
StatePublished - Jan 2018

Keywords

  • Dimensionality reduction
  • Discriminant analysis
  • Sequence classification
  • character recognition
  • metric learning

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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