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References in periodicals archive ?
In this paper we propose an unsupervised feature extraction algorithm using orthogonal wavelet transform for automatically choosing the dimensionality of features.
Parsevel's theorem states that the energy is preserved under the orthogonal wavelet transform, that is,
If f(t) be the Euclidean distance function, Parsevel's theorem also indicates that f(t) will not change by the orthogonal wavelet transform. The distance preserved property makes sure no false dismissal will occur with distance based learning algorithms [29].
Rieder with colleagues (for example, [4]) use rotation angles in the context of CORDIC-based implementation of orthogonal wavelet transforms and DCT.
S., Parameterization of orthogonal wavelet transforms and their implementation // IEEE Transactions On Circuits And Systems--II.
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