Incremental conic functions algorithm for large scale classification problems

In order to cope with classification problems involving large datasets, we propose a new mathematical programming algorithm by extending the clustering based polyhedral conic functions approach. Despite the high classification efficiency of polyhedral conic functions, the realization previously requ...

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Bibliographic Details
Published in:Digital signal processing 2018-06, Vol.77, p.187-194
Main Authors: Cimen, Emre, Ozturk, Gurkan, Gerek, Omer Nezih
Format: Article
Language:English
Subjects:
Classification
Machine learning
Mathematical programming
Polyhedral conic functions
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Summary:In order to cope with classification problems involving large datasets, we propose a new mathematical programming algorithm by extending the clustering based polyhedral conic functions approach. Despite the high classification efficiency of polyhedral conic functions, the realization previously required a nested implementation of k-means and conic function generation, which has a computational load related to the number of data points. In the proposed algorithm, an efficient data reduction method is employed to the k-means phase prior to the conic function generation step. The new method not only improves the computational efficiency of the successful conic function classifier, but also helps avoiding model over-fitting by giving fewer (but more representative) conic functions.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2017.11.010