M. Alvarez

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Hidden Markov Models have shown promising results for identification of spike sources in Parkinson's disease treatment, e.g., for deep brain stimulation. Usual classification criteria consist in maximum likelihood rule for the recognition of the correct class. In this paper, we present a different classification scheme based in proximity analysis. For this(More)
This paper presents one simple method for the direct FIR minimum-phase multiplier-free filter design. The prototype filter is a cascade of the corresponding comb and cosine filters as well as cosine prefilters, which do not require any multipliers. We use the modified sharpening technique to improve the magnitude characteristic of this cascade in both, the(More)
Kernel Principal Component analysis is a nonlinear generalization of the popular linear multivariate analysis method. However, this method assumes that the observed data is independent, a disadvantage for many practical applications. In order to overcome this difficulty, the authors propose a combination of Kernel Principal Component analysis and hidden(More)
Probabilistic Principal Component Analysis is a reformulation of the common multivariate analysis technique known as Principal Component Analysis. It employs a latent variable model framework similar to factor analysis allowing to establish a maximum likelihood solution for the parameters that comprise the model. One of the main assumptions of Probabilistic(More)
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