A Non-Linear Tensor Tracking Algorithm for Analysis of Incomplete Multi-Channel EEG Data

Linh-Trung, Nguyen; Minh-Chinh, Truong; Nguyen, Viet-Dung; Abed-Meraim, Karim

doi:10.1109/ismict.2018.8573711

Cited by 4 publications

(6 citation statements)

References 24 publications

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“… 74 and Linh-Trung et al. 75 . At each time, data of 20 (and 40) channels are assumed to be discarded randomly for our missing observation purpose.…”

Section: Resultsmentioning

confidence: 96%

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Tracking online low-rank approximations of higher-order incomplete streaming tensors

et al. 2023

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“… 74 and Linh-Trung et al. 75 . At each time, data of 20 (and 40) channels are assumed to be discarded randomly for our missing observation purpose.…”

Section: Resultsmentioning

confidence: 96%

“… 74 and Linh-Trung et al. 75 to illustrate the use of ACP for analyzing multichannel EEG signals. In this task, we use a public EEG dataset collected on 14 subjects during the stimulation of hands (EEG data: http://www.erpwavelab.org/index.htm ).…”

Section: Resultsmentioning

confidence: 99%

Tracking online low-rank approximations of higher-order incomplete streaming tensors

et al. 2023

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“…Multichannel EEG Analysis: We follow the experimental framework in [74,75] to illustrate the use of ACP for analyzing multichannel EEG signals.…”

Section: Real Datamentioning

confidence: 99%

“…Note that, the resulting time-frequency representations are reshaped into vectors of length 4392 and are hence streamed. In a nutshell, we have the EEG tensor whose size is 28 × 64 × 4392 and its rank is set at 3 as provided in [74,75]. At each time, data of 20 (and 40) channels are assumed to be discarded randomly for our missing observation purpose.…”

Section: Real Datamentioning

confidence: 99%

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Tracking Online Low-Rank Approximations of Higher-Order Incomplete Streaming Tensors

Thanh¹,

abed-meraim²,

Linh-Trung³

et al. 2023

Preprint

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Abstract: In this paper, we propose two new provable algorithms for tracking online low-rank approximations of high-order streaming tensors with missing data. The first algorithm, dubbed adaptive Tucker decomposition (ATD), minimizes a weighted recursive least-squares cost function to obtain the tensor factors and the core tensor in an efficient way, thanks to the alternating minimization framework and the randomized sketching technique. Under the Canonical Polyadic (CP) model, the second algorithm called ACP is developed as a variant of ATD when the core tensor is imposed to be identity. Both algorithms are low-complexity tensor trackers that have fast convergence and low memory storage requirements. A unified convergence analysis is presented for ATD and ACP to justify their performance. Experiments indicate that the two proposed algorithms are capable of streaming tensor decomposition with competitive performance with respect to estimation accuracy and runtime on both synthetic and real data. Code: https://github.com/thanhtbt/tensor_tracking Comment: to appear in Elsevier Patterns

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A Contemporary and Comprehensive Survey on Streaming Tensor Decomposition

Thanh

Abed-Meraim

Trung

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Tensor decomposition has been demonstrated to be successful in a wide range of applications, from neuroscience and wireless communications to social networks. In an online setting, factorizing tensors derived from multidimensional data streams is however non-trivial due to several inherent problems of real-time stream processing. In recent years, many research efforts have been dedicated to developing online techniques for decomposing such tensors, resulting in significant advances in streaming tensor decomposition or tensor tracking. This topic is emerging and enriches the literature on tensor decomposition, particularly from the data stream analystics perspective. Thus, it is imperative to carry out an overview of tensor tracking to help researchers and practitioners understand its development and achievements, summarise the current trends and advances, and identify challenging problems. In this article, we provide a contemporary and comprehensive survey on different types of tensor tracking techniques. We particularly categorize the state-of-the-art methods into three main groups: streaming CP decompositions, streaming Tucker decompositions, and streaming decompositions under other tensor formats (i.e., tensor-train, t-SVD, and BTD). In each group, we further divide the existing algorithms into sub-categories based on their main optimization framework and model architectures. Finally, we present several research challenges, open problems, and potential directions of tensor tracking in the future.

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A Non-Linear Tensor Tracking Algorithm for Analysis of Incomplete Multi-Channel EEG Data

Cited by 4 publications

References 24 publications

Tracking online low-rank approximations of higher-order incomplete streaming tensors

Tracking online low-rank approximations of higher-order incomplete streaming tensors

Tracking Online Low-Rank Approximations of Higher-Order Incomplete Streaming Tensors

A Contemporary and Comprehensive Survey on Streaming Tensor Decomposition

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