Tensor Decomposition for Multiple‐Instance Classification of High‐Order Medical Data

Papastergiou, Thomas; Zacharaki, Evangelia I.; Megalooikonomou, Vasileios

doi:10.1155/2018/8651930

Cited by 34 publications

(25 citation statements)

References 36 publications

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“…Because of its complexity, multi-faceted nature and unclear pathophysiology, there is a great difficulty in defining, early identifying and preventing frailty. Information and communications technologies [ 3 , 4 , 5 , 6 , 7 ] try to address this unmet need, many of them by monitoring the physical behavior of older people aiming at providing solutions for active and healthy aging.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Papagiannaki

Zacharaki

Kalouris

et al. 2019

Sensors

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The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative.

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Section: Introductionmentioning

confidence: 99%

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Papagiannaki

Zacharaki

Kalouris

et al. 2019

Sensors

Self Cite

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“…We can see that the HOSVD complexity grows linearly and exponentially with respect to the order Q. For low-order tensor [12,13], this complexity remains acceptable but this limitation becomes rapidly severe for high-order tensors (Q > 3).…”

Section: Hosvd Decompositionmentioning

confidence: 98%

Speeding Up of Kernel-Based Learning for High-Order Tensors

Karmouda

Boulanger

Boyer

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Supervised learning is a major task to classify datasets. In our context, we are interested into classification from highorder tensors datasets. The "curse of dimensionality" states that the complexities in terms of storage and computation grow exponentially with the order. As a consequence, the method from the state-of-art based on the Higher-Order SVD (HOSVD) works well but suffers from severe limitation in terms of complexities. In this work, we propose a fast Grassmannian kernel-based method for high-order tensor learning based on the equivalence between the Tucker and the tensortrain decompositions. Our solution is linked to the tensor network, where the aim is to break the initial high-order tensor into a collection of low-order tensors (at most 3-order). We show on several real datasets that the proposed method reaches a similar accuracy classification rate as the Grassmannian kernel-based method based on the HOSVD but for a much lower complexity.

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“…Variants of tensor networks and decompositions have been further employed for tasks such as tumor and legion detection from visual data [215], [216], showing benefits both in terms of performance and computational cost. Unsupervised coupled matrix-tensor factorization methods have also been proposed towards identifying coherent brain regions across subjects that activate when similar stimuli are provided [217].…”

Section: B Dimensionality Reduction Via Tensor Component Analysismentioning

confidence: 99%

Tensor Methods in Computer Vision and Deep Learning

et al. 2021

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Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic spaces and high-order interactions, tensors have a long history of applications in a wide span of computer vision problems. With the advent of the deep learning paradigm shift in computer vision, tensors have become even more fundamental. Indeed, essential ingredients in modern deep learning architectures, such as convolutions and attention mechanisms, can readily be considered as tensor mappings. In effect, tensor methods are increasingly finding significant applications in deep learning, including the design of memory and compute efficient network architectures, improving robustness to random noise and adversarial attacks, and aiding the theoretical understanding of deep networks.This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on visual data analysis and computer vision applications. Concretely, besides fundamental work in tensor-based visual data analysis methods, we focus on recent developments that have brought on a gradual increase of tensor methods, especially in deep learning architectures, and their implications in computer vision applications. To further enable the newcomer to grasp such concepts quickly, we provide companion Python notebooks, covering key aspects of the paper and implementing them, step-by-step with TensorLy.

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Tensor Decomposition for Multiple‐Instance Classification of High‐Order Medical Data

Cited by 34 publications

References 36 publications

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data

Speeding Up of Kernel-Based Learning for High-Order Tensors

Tensor Methods in Computer Vision and Deep Learning

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