PCA in autocorrelation space

POPOVICI, Vlad a JP THIRAN. PCA in autocorrelation space. LOS ALAMITOS: IEEE COMPUTER SOC, 2002, s. 132-135.

Základní údaje

Originální název

PCA in autocorrelation space

Autoři

POPOVICI, Vlad a JP THIRAN

Vydání

LOS ALAMITOS, IEEE COMPUTER SOC, 2002

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Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Utajení

není předmětem státního či obchodního tajemství

UT WoS

000177886900033

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Anotace

V originále

The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the autocorrelation space is a high dimensional space we are interested in reducing the dimensionality of feature vectors for the benefit of the pattern classification task. An established technique is Principal Component Analysis (PCA) which, however, cannot be applied directly in the autocorrelation space. In this paper we develop a new method for performing PCA in autocorrelation space, without explicitly computing the autocorrelations. The connections with the nonlinear PCA and possible extensions are also discussed.