Learning Entropy for Novelty Detection: A Cognitive Approach for Adaptive Filters
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F14%3A00224887" target="_blank" >RIV/68407700:21220/14:00224887 - isvavai.cz</a>
Result on the web
<a href="http://ieeeexplore.com/stamp/stamp.jsp?tp=&arnumber=6943329" target="_blank" >http://ieeeexplore.com/stamp/stamp.jsp?tp=&arnumber=6943329</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SSPD.2014.6943329" target="_blank" >10.1109/SSPD.2014.6943329</a>
Alternative languages
Result language
angličtina
Original language name
Learning Entropy for Novelty Detection: A Cognitive Approach for Adaptive Filters
Original language description
This paper recalls the practical calculation of Learning Entropy (LE) for novelty detection, extends it for various gradient techniques and discusses its use for multivariate dynamical systems with ability of distinguishing between data perturbations orsystem-function perturbations. LE has been recently introduced for novelty detection in time series via supervised incremental learning of polynomial filters, i.e. higher-order neural units (HONU). This paper demonstrates LE also on enhanced gradient descent adaptation techniques that are adopted and summarized for HONU. As an aside, LE is proposed as a new performance index of adaptive filters. Then, we discuss Principal Component Analysis and Kernel PCA for HONU as a potential method to suppress detection of data-measurement perturbations and to enforce LE for system-perturbation novelties.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
BC - Theory and management systems
OECD FORD branch
—
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Sensor Signal Processing for Defence (SSPD), 2014
ISBN
978-1-4799-5294-6
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
1-5
Publisher name
IEEE
Place of publication
New York
Event location
Edinburgh
Event date
Sep 8, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000349464000026