Sparse Representation for Time-Series Classification
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 probabilistic 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 includes the joint sparse representation that enforces collaboration across all the modalities as well as the tree-structured sparsity that provides a flexible framework for fusion of modalities at multiple granularities. Furthermore, 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 08, 2015
- Accession Number
- ADA624593
Entities
People
- Asok Ray
- Nasser M. Nasrabadi
- Soheil Bahrampour
Organizations
- Pennsylvania State University