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- W2110169306 abstract "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. During learning, the weights of the network are adapted to the temporal statistics of its input, in a way that maximizes the information retained by the network. Sequences stored in the network may be retrieved in the reverse order of presentation, thus providing a straight-forward implementation of a logical stack." @default.
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- W2110169306 date "2005-01-31" @default.
- W2110169306 modified "2023-09-25" @default.
- W2110169306 title "Recursive PCA and the structure of time series" @default.
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- W2110169306 doi "https://doi.org/10.1109/ijcnn.2004.1380899" @default.
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