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Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion(More)
We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space(More)
— We propose a framework for optimizing Deep Neural Networks (DNN) with the objective of learning low-dimensional discriminative features from high-dimensional complex patterns. In a two-stage process that effectively implements a Nonlinear Discriminant Analysis (NDA), we first pretrain a DNN using stochastic optimization, partly supervised and(More)
Eukaryotic chromatin is organized in contiguous domains that differ in protein binding, histone modifications, transcriptional activity, and in their degree of compaction. Genome-wide comparisons suggest that, overall, the chromatin organization is similar in different cells within an organism. Here, we compare the structure and activity of the 61C7-61C8(More)