Efficient Implementation of Gaussian Mixture Models Using Vote Count Circuit

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Efficient Implementation of Gaussian Mixture Models Using Vote Count Circuit
Title:
Efficient Implementation of Gaussian Mixture Models Using Vote Count Circuit
Journal Title:
Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA)
Publication Date:
09 December 2014
Citation:
W. Yang, R. Yu, W. Jiang and H. Shu, "Efficient implementation of Gaussian Mixture Models using vote count circuit," Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, Siem Reap, 2014, pp. 1-5. doi: 10.1109/APSIPA.2014.7041519
Abstract:
Vote count (VC) is a fast search algorithm originally designed for similarity search on large scale data set. VC can be efficiently implemented using simple modification to the Random Access Memory (RAM) or other memory structures such as NOR or NAND Flash memory, such that the search complexity reduces to O(1) regardless of the dimensionality of data or the size of the data set. This paper proposes a low complexity implementation for the posterior probability calculation of Gaussian Mixture Models (GMM) using the VC circuit. The performance of the proposed implementation is evaluated in terms of both accuracy of the posterior probability calculation, and classification error rate if GMM is used as a classifier.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISBN:
978-6-1636-1823-8
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