Exemplar Selection via Leave-One-Out Kernel Averaged Gradient Descent and Subtractive Clustering

Abstract : Scalable data mining and machine learning require data abstractions. This work presents a scheme for automatic selection of representative real data points as exemplars. Currently few algorithms can select representative exemplars from the data. K-medoids and Affinity Propagation are such algorithms. K-medoids requires the number of exemplars to be given in advance, as well as a dissimilarity matrix in memory. Affinity propagation automatically finds exemplars as well as their k number but it requires a similarity matrix in memory. A fast algorithm, which works without the need of any matrix in memory, is Subtractive Clustering, but it requires user-defined bandwidth parameters. The essence of the proposed solution relies on a leave-one-out kernel averaged gradient descent that automatically estimates a suitable bandwidth parameter from the data in conjunction with Subtractive Clustering algorithm that further uses this bandwidth for extracting the most representative exemplars, without initial knowledge of their number. Experimental simulations and comparisons of the proposed solution with Affinity propagation exemplar selection on various benchmark datasets seem promising.
Document type :
Conference papers
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/hal-01557629
Contributor : Hal Ifip <>
Submitted on : Thursday, July 6, 2017 - 1:55:27 PM
Last modification on : Monday, July 30, 2018 - 12:02:02 PM
Long-term archiving on : Wednesday, January 24, 2018 - 2:45:26 AM

File

430537_1_En_25_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Yiannis Kokkinos, Konstantinos Margaritis. Exemplar Selection via Leave-One-Out Kernel Averaged Gradient Descent and Subtractive Clustering. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. pp.292-304, ⟨10.1007/978-3-319-44944-9_25⟩. ⟨hal-01557629⟩

Share

Metrics

Record views

88

Files downloads

107