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• W2110176223 abstract "Capturing regularities in high-dimensional data is an important problem in machine learning and signal processing. Here we present a statistical model that learns a nonlinear representation from the data that reflects abstract, invariant properties of the signal without making requirements about the kind of signal that can be processed. The model has a hierarchy of two layers, with the first layer broadly corresponding to Independent Component Analysis (ICA) and a second layer to represent higher order structure. We estimate the model using the mathematical framework of Score Matching (SM), a novel method for the estimation of non-normalized statistical models. The model incorporates a squaring nonlinearity, which we propose to be suitable for forming a higher-order code of invariances. Additionally the squaring can be viewed as modelling subspaces to capture residual dependencies, which linear models cannot capture." @default.
• W2110176223 created "2016-06-24" @default.
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• W2110176223 date "2007-01-01" @default.
• W2110176223 modified "2023-09-27" @default.
• W2110176223 title "A Two-Layer ICA-Like Model Estimated by Score Matching" @default.
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• W2110176223 doi "https://doi.org/10.1007/978-3-540-74695-9_82" @default.
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