Hidden discriminative features extraction for supervised high-order time series modeling. (1st November 2016)

• Record Type: Journal Article
• Authors: Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee
• Is Part Of: Computers in biology and medicine. Volume 78(2016)
• Page Start: 81
• Page End: 90
• Publication Date: 2016-11-01
• Subjects: Electroencephalogram (EEG) -- Microarray data -- High-order time series -- Discriminant analysis -- Multi-way arrays -- Tucker decomposition -- Dimensionality reduction -- Seizure prediction; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.08.018
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel × frequency bin × time frame and a microarray data that is modeled as gene × sample × time ) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix-based algorithms and recent tensor-based, discriminant-decomposition approaches; this is especially the case considering the small number of samples that are used in practice. Highlights: An extension of the Tucker decomposition for high-order discriminative subspace features extraction is investigated. A robust identification of underlying discriminative characteristics leads to easy interpretability and visualization capability. Two real third-order tensor time series structures: including an epilepsy electroencephalogram (EEG) and a microarray data were used for the evaluation. The method shows superior performance in term of effectiveness, computation time compared to existing techniques.