Sparse representation for time-series classification

Soheil Bahrampour, Nasser M. Nasrabadi, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations


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
Number of pages17
ISBN (Electronic)9789813144552
ISBN (Print)9789813144545
StatePublished - Dec 15 2016

All Science Journal Classification (ASJC) codes

  • General Computer Science


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