Pseudo Bidirectional Linear Discriminant Analysis for Multivariate Time Series Classification

Zhao, Jianhua; Sun, Feng; Liang, Haiye; Ma, Xuan; Li, Xingxu; He, Jie

doi:10.1109/access.2021.3089839

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…In this subsection, we compare the classification performance of RBLDA and related competitors including BLDA [4], PBLDA [16], RLDA [18], BPCA [3] and DTW [14].…”

Section: Classification Performance On Real Mts Datasetsmentioning

confidence: 99%

“…In fact, BPCA has been suggested in [3] and the experiments show that BPCA is often advantageous over the closely related methods. Since BPCA is an unsupervised method, BLDA is further suggested in [16] to utilize the matrix data structure and label information simultaneously. The results show that the use of label information is generally beneficial to the classification of MTS data.…”

Section: Introductionmentioning

confidence: 99%

“…The results show that the use of label information is generally beneficial to the classification of MTS data. Since the existing implementations of BLDA [4,17] suffers from two problems: (i) BLDA can not be performed when one of its withinclass matrices is singular; (ii) the computational cost could be very heavy when the number of variables or time points is high, a variant of BLDA based on pseudoinverse (PBLDA) is also proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

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Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data

Zhao¹,

Liang²,

Li³

et al. 2022

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In recent years, the methods on matrix-based or bilinear discriminant analysis (BLDA) have received much attention. Despite their advantages, it has been reported that the traditional vector-based regularized LDA (RLDA) is still quite competitive and could outperform BLDA on some benchmark datasets. Nevertheless, it is also noted that this finding is mainly limited to image data. In this paper, we propose regularized BLDA (RBLDA) and further explore the comparison between RLDA and RBLDA on another type of matrix data, namely multivariate time series (MTS). Unlike image data, MTS typically consists of multiple variables measured at different time points. Although many methods for MTS data classification exist within the literature, there is relatively little work in exploring the matrix data structure of MTS data. Moreover, the existing BLDA can not be performed when one of its within-class matrices is singular. To address the two problems, we propose RBLDA for MTS data classification, where each of the two within-class matrices is regularized via one parameter. We develop an efficient implementation of RBLDA and an efficient model selection algorithm with which the cross validation procedure for RBLDA can be performed efficiently. Experiments on a number of real MTS data sets are conducted to evaluate the proposed algorithm and compare RBLDA with several closely related methods, including RLDA and BLDA. The results reveal that RBLDA achieves the best overall recognition performance and the proposed model selection algorithm is efficient; Moreover, RBLDA can produce better visualization of MTS data than RLDA.

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“…In this subsection, we compare the classification performance of RBLDA and related competitors including BLDA [4], PBLDA [16], RLDA [18], BPCA [3] and DTW [14].…”

Section: Classification Performance On Real Mts Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%