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Principal component analysis (PCA) is one of the most widely used dimension reduction and multivariate statistical techniques. From a probabilistic perspective, PCA seeks a low-dimensional representation of data in the presence of…
There are a number of well-established methods such as principal components analysis (PCA) for automatically capturing systematic variation due to latent variables in large-scale genomic data. PCA and related methods may directly provide a…
We report on the automated classification of Hipparcos variable stars by a supervised classification algorithm known as Support Vector Machines. The dataset comprised about 3200 stars, each characterized by 51 features. These are the B-V…
This work studies the recursive robust principal components' analysis (PCA) problem. Here, "robust" refers to robustness to both independent and correlated sparse outliers, although we focus on the latter. A key application where this…
Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise. The maximum likelihood solution for the model is an eigenvalue problem on the…
This paper introduces a novel variability report generator developed for the Large Array Survey Telescope (LAST), a cost-effective multi-purpose telescope array conducting a wide survey of the variable sky in the visible-light spectrum.…
In the last decade we have seen an enormous increase in the size and quality of spectroscopic galaxy surveys, both at low and high redshift. New statistical techniques to analyse large portions of galaxy spectra are now finding favour over…
I present here a review of past and present multi-disciplinary research of the Pittsburgh Computational AstroStatistics (PiCA) group. This group is dedicated to developing fast and efficient statistical algorithms for analysing huge…
Insights on stellar surface large-scale magnetic field topologies are usually drawn by applying Zeeman-Doppler-Imaging (ZDI) to the observed spectropolarimetric time series. However, ZDI requires experience for reliable results to be…
Brillouin microscopy has recently emerged as a new bio-imaging modality that provides information on the micromechanical properties of biological materials, cells and tissues. The data collected in a typical Brillouin microscopy experiment…
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA…
Stars exhibit a bewildering variety of variable behaviors ranging from explosive magnetic flares to stochastically changing accretion to periodic pulsations or rotations. The principal LSST surveys will have cadences too sparse and…
When applying principal component analysis (PCA) for dimension reduction, the most varying projections are usually used in order to retain most of the information. For the purpose of anomaly and change detection, however, the least varying…
We present VIVACE, the VIrac VAriable Classification Ensemble, a catalogue of variable stars extracted from an automated classification pipeline for the Vista Variables in the V\'ia L\'actea (VVV) infrared survey of the Galactic bar/bulge…
Cellular Automata are discrete dynamical systems that evolve following simple and local rules. Despite of its local simplicity, knowledge discovery in CA is a NP problem. This is the main motivation for using data mining techniques for CA…
Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduction with numerous applications in science and engineering. However, the standard PCA suffers from the fact that the principal components…
Principal component analysis is a powerful statistical system to investigate the structure and dynamics of the molecular interstellar medium, with particular emphasis on the study of turbulence, as revealed by spectroscopic imaging of…
Principal component analysis (PCA) is a popular method for projecting data onto uncorrelated components in lower dimension, although the optimal number of components is not specified. Likewise, multiple signal classification (MUSIC)…
We have shown previously that a broad correlation between the peak radio luminosity and the variability time-scales, approximately L ~ t^5, exists for variable synchrotron emitting sources and that different classes of astrophysical source…
In recent years, the cross spectrum has received considerable attention as a means of characterising the variability of astronomical sources as a function of wavelength. While much has been written about the statistics of time and phase…