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Consensus Clustering Using kNN Mode Seeking
(Chapter; Bokkapittel, 2015-06-09)
In this paper we present a novel clustering approach which combines two modern strategies, namely consensus clustering, and two stage clustering as represented by the mean shift spectral clustering algorithm. We introduce the recent kNN mode seeking algorithm in the consensus clustering framework, and the information theoretic kNN Cauchy Schwarz divergence as foundation for spectral clustering. In ...
Deep kernelized autoencoders
(Peer reviewed; Book; Bokkapittel; Bok; Chapter, 2017-05-19)
In this paper we introduce the deep kernelized autoencoder,
a neural network model that allows an explicit approximation of (i) the
mapping from an input space to an arbitrary, user-specified kernel space
and (ii) the back-projection from such a kernel space to input space. The
proposed method is based on traditional autoencoders and is trained
through a new unsupervised loss function. ...
Temporal overdrive recurrent neural network
(Chapter; Bokkapittel, 2017-07-03)
In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics. The proposed network is composed by several recurrent groups of neurons that are trained to separately adapt to each timescale, in order to improve the system identification process. We test our framework on time series prediction tasks ...
Critical echo state network dynamics by means of Fisher information maximization
(Chapter; Bokkapittel, 2017-07-03)
The computational capability of an Echo State Network (ESN), expressed in terms of low prediction error and high short-term memory capacity, is maximized on the so-called “edge of criticality”. In this paper we present a novel, unsupervised approach to identify this edge and, accordingly, we determine hyperparameters configuration that maximize network performance. The proposed method is ...