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Recursive principal components analysis

✍ Scribed by Thomas Voegtlin

Publisher: Elsevier Science
Year: 2005
Tongue: English
Weight: 273 KB
Volume: 18
Category: Article
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2005.07.005

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✦ Synopsis

A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by the network are adapted to the temporal statistics of the input. Moreover, sequences stored in the network may be retrieved explicitly, in the reverse order of presentation, thus providing a straight-forward neural implementation of a logical stack.

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