256 research outputs found
Fully digital encryption technique
We propose an alternative fully digital encryption technique based on using the Fourier transform of the original object to be processed and a speckled reference wave as encryption mask. Once encrypted, the Fourier transform spectrum of the object is holographically stored. The original data recovering is performed by digital reconstruction using the same encryption mask, which is also holographically stored. Quality of reconstructed data is evaluated as a function of the sensed encrypted data. Computer simulations and experimental results are presented to demonstrate the method
Sparse Linear Identifiable Multivariate Modeling
In this paper we consider sparse and identifiable linear latent variable
(factor) and linear Bayesian network models for parsimonious analysis of
multivariate data. We propose a computationally efficient method for joint
parameter and model inference, and model comparison. It consists of a fully
Bayesian hierarchy for sparse models using slab and spike priors (two-component
delta-function and continuous mixtures), non-Gaussian latent factors and a
stochastic search over the ordering of the variables. The framework, which we
call SLIM (Sparse Linear Identifiable Multivariate modeling), is validated and
bench-marked on artificial and real biological data sets. SLIM is closest in
spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in
inference, Bayesian network structure learning and model comparison.
Experimentally, SLIM performs equally well or better than LiNGAM with
comparable computational complexity. We attribute this mainly to the stochastic
search strategy used, and to parsimony (sparsity and identifiability), which is
an explicit part of the model. We propose two extensions to the basic i.i.d.
linear framework: non-linear dependence on observed variables, called SNIM
(Sparse Non-linear Identifiable Multivariate modeling) and allowing for
correlations between latent variables, called CSLIM (Correlated SLIM), for the
temporal and/or spatial data. The source code and scripts are available from
http://cogsys.imm.dtu.dk/slim/.Comment: 45 pages, 17 figure
Semi-Supervised Kernel PCA
We present three generalisations of Kernel Principal Components Analysis
(KPCA) which incorporate knowledge of the class labels of a subset of the data
points. The first, MV-KPCA, penalises within class variances similar to Fisher
discriminant analysis. The second, LSKPCA is a hybrid of least squares
regression and kernel PCA. The final LR-KPCA is an iteratively reweighted
version of the previous which achieves a sigmoid loss function on the labeled
points. We provide a theoretical risk bound as well as illustrative experiments
on real and toy data sets
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