Discriminant Analysis on a Stream of Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00357775" target="_blank" >RIV/68407700:21240/22:00357775 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Discriminant Analysis on a Stream of Features
Original language description
Online learning is a well-established problem in machine learning. But while online learning is commonly concerned with learning on a stream of samples, this article is concerned with learning on a stream on features. An online quadratic discriminant analysis (QDA) is proposed because it is fast, capable of modeling feature interactions, and it can still return an exact solution. When a new feature is inserted into a training set, the proposed implementation of QDA showed a 1000-fold speed up to scikit-learn QDA. Fast learning on a stream of features provides a data scientist with timely feedback about the importance of new features during the feature engineering phase. In the production phase, it reduces the cost of updating a model when a new source of potentially useful features appears.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Engineering Applications of Neural Networks
ISBN
978-3-031-08222-1
ISSN
1865-0929
e-ISSN
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Number of pages
12
Pages from-to
223-234
Publisher name
Springer, Cham
Place of publication
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Event location
Limenas Hersonissou 700 14 Crete
Event date
Jun 17, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000926169100019