Arnau Tibau Puig

Learn More
The scalar shrinkage-thresholding operator is a key ingredient in variable selection algorithms arising in wavelet denoising, JPEG2000 image compression and predictive analysis of gene microarray data. In these applications, the decision to select a scalar variable is given as the solution to a scalar sparsity penalized quadratic optimization. In some other(More)
The scalar shrinkage-thresholding operator (SSTO) is a key ingredient of many modern statistical signal processing algorithms including: sparse inverse problem solutions, wavelet denoising, and JPEG2000 image compression. In these applications, it is customary to select the threshold of the operator by solving a scalar sparsity penalized quadratic(More)
We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence-ordering arise in applications where variables are activated(More)
We present a novel factor analysis method that can be applied to discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence ordering arise in applications where variables are activated in a(More)
Networks are ubiquitous in science, serving as a natural representation for many complex physical, biological, and social phenomena. Significant efforts have been dedicated to analyzing such network representations to reveal their structure and provide some insight towards the phenomena of interest. Computational methods for analyzing networks have(More)
Principal Component Analysis (PCA) is a widely applied method for extracting structure from samples of high dimensional biological data. Often there exist misalignments between different samples and this can cause severe problems in PCA if not properly taken into account. For example, subject-dependent temporal differences in gene expression response to a(More)
We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence-ordering arise in applications where variables are activated(More)
We present a factor analysis method that accounts for possible temporal misalignment of the factor loadings across the population of samples. Our main hypothesis is that the data contains a subset of variables with similar but delayed profiles obeying a consistent precedence ordering relationship. Our model is motivated by the difficulty of gene expression(More)
  • 1