Feature Selection with Limited Bit Depth Mutual Information for Embedded Systems
Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big Data? Should it depend on the numerical representation of the machine? Since portable em...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-09-01
|
Series: | Proceedings |
Subjects: | |
Online Access: | http://www.mdpi.com/2504-3900/2/18/1187 |
id |
doaj-7f3a2efac154490fb02a212dd13aec5d |
---|---|
record_format |
Article |
spelling |
doaj-7f3a2efac154490fb02a212dd13aec5d2020-11-25T00:57:50ZengMDPI AGProceedings2504-39002018-09-01218118710.3390/proceedings2181187proceedings2181187Feature Selection with Limited Bit Depth Mutual Information for Embedded SystemsLaura Morán-Fernández0Verónica Bolón-Canedo1Amparo Alonso-Betanzos2CITIC, Universidade da Coruña, 15071 A Coruña, SpainCITIC, Universidade da Coruña, 15071 A Coruña, SpainCITIC, Universidade da Coruña, 15071 A Coruña, SpainData is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big Data? Should it depend on the numerical representation of the machine? Since portable embedded systems have been growing in importance, there is also increased interest in implementing machine learning algorithms with a limited number of bits. Not only learning, also feature selection, most of the times a mandatory preprocessing step in machine learning, is often constrained by the available computational resources. In this work, we consider mutual information—one of the most common measures of dependence used in feature selection algorithms—with reduced precision parameters.http://www.mdpi.com/2504-3900/2/18/1187feature selectionmutual informationreduced precisionembedded systemsBig Data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Laura Morán-Fernández Verónica Bolón-Canedo Amparo Alonso-Betanzos |
spellingShingle |
Laura Morán-Fernández Verónica Bolón-Canedo Amparo Alonso-Betanzos Feature Selection with Limited Bit Depth Mutual Information for Embedded Systems Proceedings feature selection mutual information reduced precision embedded systems Big Data |
author_facet |
Laura Morán-Fernández Verónica Bolón-Canedo Amparo Alonso-Betanzos |
author_sort |
Laura Morán-Fernández |
title |
Feature Selection with Limited Bit Depth Mutual Information for Embedded Systems |
title_short |
Feature Selection with Limited Bit Depth Mutual Information for Embedded Systems |
title_full |
Feature Selection with Limited Bit Depth Mutual Information for Embedded Systems |
title_fullStr |
Feature Selection with Limited Bit Depth Mutual Information for Embedded Systems |
title_full_unstemmed |
Feature Selection with Limited Bit Depth Mutual Information for Embedded Systems |
title_sort |
feature selection with limited bit depth mutual information for embedded systems |
publisher |
MDPI AG |
series |
Proceedings |
issn |
2504-3900 |
publishDate |
2018-09-01 |
description |
Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big Data? Should it depend on the numerical representation of the machine? Since portable embedded systems have been growing in importance, there is also increased interest in implementing machine learning algorithms with a limited number of bits. Not only learning, also feature selection, most of the times a mandatory preprocessing step in machine learning, is often constrained by the available computational resources. In this work, we consider mutual information—one of the most common measures of dependence used in feature selection algorithms—with reduced precision parameters. |
topic |
feature selection mutual information reduced precision embedded systems Big Data |
url |
http://www.mdpi.com/2504-3900/2/18/1187 |
work_keys_str_mv |
AT lauramoranfernandez featureselectionwithlimitedbitdepthmutualinformationforembeddedsystems AT veronicaboloncanedo featureselectionwithlimitedbitdepthmutualinformationforembeddedsystems AT amparoalonsobetanzos featureselectionwithlimitedbitdepthmutualinformationforembeddedsystems |
_version_ |
1725222650054180864 |