Sparse representation for time-series classification

Soheil Bahrampour, Nasser M. Nasrabadi, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter studies the problem of time-series classification and presents an overview of recent developments in the area of feature extraction and information fusion. In particular, a recently proposed feature extraction algorithm, namely symbolic dynamic filtering (SDF), is reviewed. The SDF algorithm generates low-dimensional feature vectors using proba- bilistic finite state automata that are well-suited for discriminative tasks. The chapter also presents the recent developments in the area of sparse- representation-based algorithms for multimodal classification. This in- cludes the joint sparse representation that enforces collaboration across all the modalities as well as the tree-structured sparsity that provides a exible framework for fusion of modalities at multiple granularities. Fur- thermore, unsupervised and supervised dictionary learning algorithms are reviewed. The performance of the algorithms are evaluated on a set of field data that consist of passive infrared and seismic sensors.

Original languageEnglish (US)
Title of host publicationPattern Recognition and Big Data
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages199-215
Number of pages17
ISBN (Electronic)9789813144552
ISBN (Print)9789813144545
DOIs
StatePublished - Dec 15 2016

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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    Bahrampour, S., Nasrabadi, N. M., & Ray, A. (2016). Sparse representation for time-series classification. In Pattern Recognition and Big Data (pp. 199-215). World Scientific Publishing Co. Pte Ltd. https://doi.org/10.1142/9789813144552_0007