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Rank-Revealing Orthogonal Decomposition in Extreme Learning Machine Design Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Fast Supervised Selection of Prototypes for Metric-Based Learning

Author : Lluís A. Belanche

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

A crucial factor for successful learning is the finding of more convenient representations for a problem, such that subsequent processing can be delivered to linear or non-linear modeling methods. Similarity functions are a flexible way to express knowledge about a problem and to capture meaningful relations of data in input space. In this paper we use similarity functions to find an alternative data representation which is then reduced by selecting a subset of relevant prototypes, in a supervised way. The idea is tested in a set of modelling problems, characterized by a mixture of data types and different amounts of missing values. The results demonstrate competitive or better performance than traditional methods in terms of prediction error and sparsity of the representation.

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Footnotes
1
For example, by the presence of missing values, by the feature semantics, etc.
 
2
Such variables are increasingly common, especially when they refer to a time periodicity, such as the month in a year.
 
3
It is not difficult to check that this is equivalent to the replacement of the missing similarities by the average of the non-missing ones. Therefore, the conjecture is that the missing values, if known, would not change the overall similarity significantly.
 
4
This property is not used in this work but it is interesting in other contexts, such as optimization.
 
5
The experiments were run on a HP laptop with 2GB of RAM and an Intel(R) Core(TM)2 Duo CPU T7500 at 2.20GHz.
 
6
See the caption of Table 1 for a description.
 
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Metadata
Title
Fast Supervised Selection of Prototypes for Metric-Based Learning
Author
Lluís A. Belanche
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01420-9

Electronic ISBN: 978-3-030-01421-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01421-6_55

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