709 research outputs found
BMICA-independent component analysis based on B-spline mutual information estimator
The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis)
exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA
High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis
Independent Component Analysis (ICA) is a dimensionality reduction technique
that can boost efficiency of machine learning models that deal with probability
density functions, e.g. Bayesian neural networks. Algorithms that implement
adaptive ICA converge slower than their nonadaptive counterparts, however, they
are capable of tracking changes in underlying distributions of input features.
This intrinsically slow convergence of adaptive methods combined with existing
hardware implementations that operate at very low clock frequencies necessitate
fundamental improvements in both algorithm and hardware design. This paper
presents an algorithm that allows efficient hardware implementation of ICA.
Compared to previous work, our FPGA implementation of adaptive ICA improves
clock frequency by at least one order of magnitude and throughput by at least
two orders of magnitude. Our proposed algorithm is not limited to ICA and can
be used in various machine learning problems that use stochastic gradient
descent optimization
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