NDLI: Using weak supervision in learning Gaussian mixture models

Content Provider	IEEE Xplore Digital Library
Author	Ghosh, S. Srinivasan, S. Andrews, B.
Copyright Year	2009
Description	Author affiliation: Department of Computer Science, University of Colorado, Boulder, 80309, USA (Ghosh, S.) \|\| Research and Technology Center, Robert Bosch LLC, Pittsburgh, PA 15212, USA (Srinivasan, S.; Andrews, B.)
Abstract	The expectation maximization algorithm is a popular approach to learning Gaussian mixture models from unlabeled data. In addition to the unlabeled data, in many applications, additional sources of information such as a-priori knowledge of mixing proportions are also available. We present a weakly supervised approach, in the form of a penalized expectation maximization algorithm that uses a-priori knowledge to guide the model training process. The algorithm penalizes those models whose predicted mixing proportions have high divergence from the a-priori mixing proportions. We also present an extension to incorporate both labeled and unlabeled data in a semi-supervised setting. Systematic evaluations on several publicly available datasets show that the proposed algorithms outperforms the expectation maximization algorithm. The performance gains are particularly significant when the amount of unlabeled data is limited and in the presence of noise.
Starting Page	973
Ending Page	979
File Size	5511062
Page Count	7
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178922
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Acoustic noise Clustering algorithms Noise robustness Biological system modeling Calibration Neural networks Information resources Performance gain Data models
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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