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Semisupervised Sparse Multilinear Discriminant Analysis

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Abstract

Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size (SSS) and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms (ECGs), another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis (SSSMDA) method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation algorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation (STFT) to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.

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Authors and Affiliations

  1. MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Kai Huang & Li-Qing Zhang

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  1. Kai Huang
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  2. Li-Qing Zhang
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Correspondence to Kai Huang.

Additional information

The work was supported by the National Natural Science Foundation of China under Grant Nos. 91120305, 61272251, and the National Basic Research 973 Program of China under Grant No. 2015CB856004.

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Huang, K., Zhang, LQ. Semisupervised Sparse Multilinear Discriminant Analysis. J. Comput. Sci. Technol. 29, 1058–1071 (2014). https://doi.org/10.1007/s11390-014-1490-1

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  • DOI: https://doi.org/10.1007/s11390-014-1490-1

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