Abstract
The Kernel k-Means algorithm for clustering extends the classic k-Means clustering algorithm. It uses the kernel trick to implicitly calculate distances on a higher dimensional space, thus overcoming the classic algorithm's inability to handle data that are not linearly separable. Given a set of n elements to cluster, the n × n kernel matrix is calculated, which contains the dot products in the higher dimensional space of every possible combination of two elements. This matrix is then referenced to calculate the distance between an element and a cluster center, as per classic k-Means. In this paper, we propose a novel algorithm for zeroing elements of the kernel matrix, thus trimming the matrix, which results in reduced memory complexity and improved clustering performance.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Image Processing (ICIP 2015) |
Subtitle of host publication | Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 2285-2289 |
Number of pages | 5 |
ISBN (Electronic) | 9781479983391 |
ISBN (Print) | 9781479983407 |
DOIs | |
Publication status | Published - Jan 2016 |
Event | 2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada Duration: 27 Sep 2015 → 30 Sep 2015 |
Conference
Conference | 2015 IEEE International Conference on Image Processing (ICIP) |
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Country/Territory | Canada |
City | Quebec City, ON |
Period | 27/09/15 → 30/09/15 |