Accelerating Online CP Decompositions for Higher Order Tensors

Zhou, Shuo; Vinh, Nguyen X.; Bailey, James E.; Jia, Yunzhe; Davidson, Ian

doi:10.1145/2939672.2939763

Cited by 116 publications

(85 citation statements)

References 25 publications

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“…In this experiment, four baselines have been selected as the competitors to evaluate the performance. OnlineCP [27]: It is online CP decomposition method, where the latent factors are updated when there are new data. SambaTen [7]: Samplingbased Batch Incremental Tensor Decomposition algorithm is the most recent and state-of-the-art method in online computation of canonical parafac and perform all computations in the reduced summary space.…”

Section: Baselinesmentioning

confidence: 99%

See 1 more Smart Citation

OCTEN: Online Compression-Based Tensor Decomposition

Gujral

Pasricha

Yang

et al. 2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Tensor decompositions are powerful tools for large data analytics as they jointly model multiple aspects of data into one framework and enable the discovery of the latent structures and higher-order correlations within the data. One of the most widely studied and used decompositions, especially in data mining and machine learning, is the Canonical Polyadic or CP decomposition. However, today's datasets are not static and these datasets often dynamically growing and changing with time. To operate on such large data, we present OCTen the first ever compression-based online parallel implementation for the CP decomposition.We conduct an extensive empirical analysis of the algorithms in terms of fitness, memory used and CPU time, and in order to demonstrate the compression and scalability of the method, we apply OCTen to big tensor data. Indicatively, OCTen performs on-par or better than state-of-the-art online and offline methods in terms of decomposition accuracy and efficiency, while saving up to 40-200 % memory space.

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Section: Baselinesmentioning

confidence: 99%

“…The Zhou, el at. [27] describes an online CP decomposition method, where the latent components are updated for incoming data. The most related work to ours was proposed by [7] which is sampling-based batch incremental tensor decomposition algorithm.…”

Section: Related Workmentioning

confidence: 99%

OCTEN: Online Compression-Based Tensor Decomposition

Gujral

Pasricha

Yang

et al. 2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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show abstract

“…The second benefit of using a CP decomposition approach lies in its flexibility to be used for online learning. In particular, Zhou et al [23] have proposed a technique known as onlineCP, which allows the arrival of new data to be placed in C-space. In this way once the SVM has been trained on C data, there is no need to re-update the underlying model in time, all that needs to be done is place each new data point into C-space.…”

Section: B Tensor Analysis For Shm Datamentioning

confidence: 99%

A Tensor-based Structural Health Monitoring Approach for Aeroservoelastic Systems

Cheema

Nguyen

Kidd

et al. 2019

AIAA Scitech 2019 Forum

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Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is usually from sensor arrays, which results in data which are highly redundant and correlated, an area in which traditional two-way matrix approaches have had difficulty in deconstructing and interpreting. Newer methods involving tensor analysis allow us to analyse this multi-way structural data in a coherent manner. In our approach, we demonstrate the usefulness of tensor-based learning coupled with for damage detection, on a novel N-DoF Lagrangian aeroservoelastic model.

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“…Additionally, MF incremental algorithms have been developed by Gemulla et al [8], Pálovics et al [17], Sarwar et al [20], Takács et al [23], Vinagre et al [25], Zheng and Xie [29]. Tensor dynamic implementations were also provided by Kasai [14], Song et al [22], Zhou et al [30].…”

Section: Related Workmentioning

confidence: 99%

Incremental Matrix Co-factorization for Recommender Systems with Implicit Feedback

Anyosa

Vinagre

Jorge

2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Recommender systems try to predict which items a user will prefer. Traditional models for recommendation only take into account the user-item interaction, usually expressed by explicit ratings. However, in these days, web services continuously generate auxiliary data from users and items that can be incorporated into the recommendation model to improve recommendations. In this work, we propose an incremental Matrix Co-factorization model with implicit user feedback, considering a real-world data-stream scenario. This model can be seen as an extension of the conventional Matrix Factorization that includes additional dimensions to be decomposed in the common latent factor space. We test our proposal against a baseline algorithm that relies exclusively on interaction data, using prequential evaluation. Our experimental results show a significant improvement in the accuracy of recommendations, after incorporating an additional dimension in three music domain datasets.

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Accelerating Online CP Decompositions for Higher Order Tensors

Cited by 116 publications

References 25 publications

OCTEN: Online Compression-Based Tensor Decomposition

OCTEN: Online Compression-Based Tensor Decomposition

A Tensor-based Structural Health Monitoring Approach for Aeroservoelastic Systems

Incremental Matrix Co-factorization for Recommender Systems with Implicit Feedback

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