Greedy Kernel Principal Component Analysis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F06%3A03124623" target="_blank" >RIV/68407700:21230/06:03124623 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Greedy Kernel Principal Component Analysis
Original language description
This contribution discusses one aspect of statistical learning and generalization. Theory of learning is very relevant to cognitive systems including cognitive vision. A technique allowing to approximate a huge training set is proposed. The approach aimsto represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation are properly selected vectors from the training set and not as their linear combinations. The basis vectors can be selected by a simple algorithm which has low computational requirements and allows on-line processing of huge data sets. As the computations inthe proposed algorithm appear in a form of dot product, kernel methods can be used to cope with non-linear problems. The proposed method was tested to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discr
Czech name
Není k dispozici
Czech description
Není k dispozici

Project
<a href="/en/project/GA102%2F03%2F0440" target="_blank" >GA102/03/0440: Recognizing human activities for automated video surveillance</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2006
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)