Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Multidimensional optimization holds a central role in many machine learning problems. When a model quality functional is measured with an almost arbitrary external noise, it makes sense to use randomized optimization techniques. This paper deals with the problem of clustering of a Gaussian mixture model under unknown but bounded disturbances. We introduce a stochastic approximation algorithm with randomly perturbed input (like SPSA) to solve this problem. The proposed method is appropriate for the online learning with streaming data, and it has a high speed of convergence. We study the conditions of the SPSA clustering algorithm applicability and show illustrative examples.
Original language | English |
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Title of host publication | 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017) |
Publisher | IEEE Canada |
Pages | 1740-1745 |
Number of pages | 6 |
State | Published - 2017 |
Event | 1st Annual IEEE Conference on Control Technology and Applications - Hawaii, United States Duration: 27 Aug 2017 → 30 Aug 2017 |
Conference | 1st Annual IEEE Conference on Control Technology and Applications |
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Country/Territory | United States |
Period | 27/08/17 → 30/08/17 |
ID: 32479432