Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries

Oh, Seok-Jin; Park, Namyong; Sael, Lee; Kang, U

doi:10.1109/icde.2018.00104

Cited by 42 publications

(41 citation statements)

References 40 publications

(82 reference statements)

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“…Recently, several scalable algorithms were proposed in [116]- [118] for computation of the HOSVD. These algorithms inherently do not have a randomized structure and basically they exploit the idea of on-the-fly-computation and parallel row-wise update rule roles to avoid the intermediate data explosion problem.…”

Section: Discussion On Further Challengesmentioning

confidence: 99%

Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD)

et al. 2021

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Big data analysis has become a crucial part of new emerging technologies such as the internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among many other techniques, dimensionality reduction plays a key role in such analyses and facilitates feature selection and feature extraction. Randomized algorithms are efficient tools for handling big data tensors. They accelerate decomposing large-scale data tensors by reducing the computational complexity of deterministic algorithms and the communication among different levels of memory hierarchy, which is the main bottleneck in modern computing environments and architectures. In this paper, we review recent advances in randomization for computation of Tucker decomposition and Higher Order SVD (HOSVD). We discuss random projection and sampling approaches, single-pass and multi-pass randomized algorithms and how to utilize them in the computation of the Tucker decomposition and the HOSVD. Simulations on synthetic and real datasets are provided to compare the performance of some of best and most promising algorithms.

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Section: Discussion On Further Challengesmentioning

confidence: 99%

Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD)

et al. 2021

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“…For example, a high-order tensor usually has enormous elements, which require high computing capability to process data. Efficient tensor decomposition algorithms have been recently proposed in [27][28][29], which can achieve up to 14 times faster tensor decomposition. How to further accelerate tensor decomposition and reduce tensor data size as far as possible is still an open problem, which deserves significant efforts in future work.…”

Section: Discussionmentioning

confidence: 99%

A Unified Fourth-Order Tensor-Based Smart Community System

Liu

Wang

et al. 2020

Sensors

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Empowered by the ubiquitous sensing capabilities of Internet of Things (IoT) technologies, smart communities could benefit our daily life in many aspects. Various smart community studies and practices have been conducted, especially in China thanks to the government’s support. However, most intelligent systems are designed and built individually by different manufacturers in diverging platforms with different functionalities. Therefore, multiple individual systems must be deployed in a smart community to have a set of functions, which could lead to hardware waste, high energy consumption and high deployment cost. More importantly, current smart community systems mainly focus on the technologies involved, while the effects of human activity are neglected. In this paper, a fourth-order tensor model representing object, time, location and human activity is proposed for human-centered smart communities, based on which a unified smart community system is designed. Thanks to the powerful data management abilities of a high-order tensor, multiple functions can be integrated into our system. In addition, since the tensor model embeds human activity information, complex functions could be implemented by exploring the effects of human activity. Two exemplary applications are presented to demonstrate the flexibility of the proposed unified fourth-order tensor-based smart community system.

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“…• Gtensor uses a row-wise update rule [10,11] Thanks to its careful design, Gtensor outperforms existing tensor analysis library in terms of running time and accuracy, as we show in Section 2.1.2.…”

Section: System Overviewmentioning

confidence: 90%

“…• Tensor decomposition: PARAFAC, nonnegative PARAFAC, Tucker [5,10,11], nonnegative Tucker, and CMTF (Coupled Matrix-Tensor Factorization) [3,6]. • Tensor generation: generating 1) tensors filled with one or random values, 2) tensors from given factor matrices and a core tensor, and 3) sparse R-MAT and Kronecker tensors.…”

Section: Concept Discovery Trend Analysismentioning

confidence: 99%

Gtensor

Ahn

Son

Kang

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Given a large tensor, how can we analyze it efficiently? Multidimensional arrays or tensors have been widely used to model real-world data. Tensor decomposition plays an important role in analyzing trends and major factors in tensors. While several tensor analysis tools have been developed, they show slow running time and limited scalability due to their heavy computational requirements. In this paper, we propose Gtensor, a fast and accurate tensor mining library using GPUs. Gtensor is carefully designed to utilize GPUs in addition to CPUs for fast analysis of tensors. Gtensor provides versatile tensor algebra tools including tensor decomposition, tensor generation, tensor-tensor operation, and tensor manipulation. We invite our audience to interact with Gtensor and analyze real-world tensor data in the domains of movie recommendation, place recommendations, and academic citation. CCS CONCEPTS • Computing methodologies → Massively parallel algorithms; • Information systems → Data mining.

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Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries

Cited by 42 publications

References 40 publications

Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD)

Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD)

A Unified Fourth-Order Tensor-Based Smart Community System

Gtensor

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