Distributed Nonnegative Tensor Canonical Polyadic Decomposition With Automatic Rank Determination
ID:83 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:00 Hits:520 Oral Presentation

Start Time:2020-06-09 14:00(Asia/Shanghai)

Duration:20min

Session:R Regular Session » R04Computational and Optimization Techniques for Multi-Channel Processing

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Abstract
Nonnegative tensor canonical polyadic decomposition (CPD) has found wide-spread applications in various signal processing tasks. However, the implementation of most existing algorithms needs the knowledge of tensor rank, which is difficult to acquire. To address this issue, by interpreting the nonnegative CPD problem using probability density functions (pdfs), a novel centralized inference algorithm is developed with an integrated feature of automatic rank determination. Furthermore, to scale the inference algorithm to massive data, its implementation under modern distributed computing architecture is investigated, giving rise to a distributed probabilistic nonnegative tensor CPD algorithm. Numerical studies using synthetic data and real-world data are presented to show the remarkable performance of the proposed algorithms in terms of accuracy and scalability.
Keywords
Speaker
Lei Cheng
Shenzhen Research Institute of Big Data, Chinese University of Hong Kong (Shenzhen), Hong Kong

Submission Author
Lei Cheng Shenzhen Research Institute of Big Data, Chinese University of Hong Kong (Shenzhen), Hong Kong
Xueke Tong The University of Hong Kong, Hong Kong
Yik-Chung Wu The University of Hong Kong, Hong Kong
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  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

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  • Dec 31 2020

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