Mitigating Outliers for Bayesian Mixture of Factor Analyzers
ID:73 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:00 Hits:451 Oral Presentation

Start Time:2020-06-09 14:15(Asia/Shanghai)

Duration:15min

Session:R Regular Session » R02Compressed Sensing and Sparse Signal Processing

Video No Permission

Tips: Only the registered participant can access the file. Please sign in first.

Abstract
The Bayesian mixture of factor analyzers (BMFA), which achieves joint clustering and dimensionality reduction, is with an appealing feature of automatic hyper-parameter learning. In addition to its great success in various unsupervised learning tasks, it exemplifies how the Bayesian statistics can be leveraged to achieve automatic hyper-parameter learning, which is an open problem of modern simultaneous (deep) dimensionality reduction and clustering. Due to the importance of the BMFA, in this paper, its mechanism is carefully investigated, and a robust variant of the BMFA that can mitigate potential outliers is further proposed. Numerical studies are presented to show the remarkable performance of the proposed algorithm in terms of accuracy and robustness.
Keywords
Speaker
Zhongtao Chen
The Chinese University of Hong Kong, Shenzhen & Shenzhen Research Institute of Big Data, China

Submission Author
Zhongtao Chen The Chinese University of Hong Kong, Shenzhen & Shenzhen Research Institute of Big Data, China
Lei Cheng Shenzhen Research Institute of Big Data, Chinese University of Hong Kong (Shenzhen), Hong Kong
Submit Comment
Verify Code Change Another
All Comments
Important Date
  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

    Early Bird Registration

  • Dec 31 2020

    Registration deadline

Sponsored By
IEEE Signal Processing Society
Contact Information