Related papers: Structural properties and classification of variab…
We consider the identifiability theory of probabilistic models and establish sufficient conditions under which the representations learned by a very broad family of conditional energy-based models are unique in function space, up to a…
In this paper we present a catalogue of the Fourier parameters for the light curves of Large Magellanic Cloud fundamental mode Cepheids in the OGLE database. These Fourier parameters are obtained with a simulated annealing method. The…
The properties of the Milky Way's nuclear stellar disc give crucial information on the epoch of bar formation. Mira variables are promising bright candidates to study the nuclear stellar disc, and through their period-age relation dissect…
Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals. In order to obtain a higher signal-to-noise ratio, the…
Independent Component Analysis (ICA) is an algorithm originally developed for finding separate sources in a mixed signal, such as a recording of multiple people in the same room speaking at the same time. Unlike Principal Component Analysis…
The Vista Magellanic Cloud (VMC, PI M.R. Cioni) survey is collecting $K_S$-band time series photometry of the system formed by the two Magellanic Clouds (MC) and the "bridge" that connects them. These data are used to build $K_S$-band light…
Independent component analysis (ICA) is a powerful tool for decomposing a multivariate signal or distribution into fully independent sources, not just uncorrelated ones. Unfortunately, most approaches to ICA are not robust against outliers.…
Independent Component Analysis (ICA) recently has attracted attention in the statistical literature as an alternative to elliptical models. Whereas k-dimensional elliptical densities depend on one single unspecified radial density, however,…
We have performed a cross-identification between OGLE-II data and single-epoch SIRIUS JHK survey data in the LMC and SMC. After eliminating obvious spurious variables, we determined the pulsation periods for 9,681 and 2,927 variables in the…
The VISTA survey of the Magellanic Clouds System (VMC) is collecting deep $K_\mathrm{s}$--band time--series photometry of the pulsating variable stars hosted in the system formed by the two Magellanic Clouds and the Bridge connecting them.…
Independent Component Analysis (ICA) is a foundational tool for unsupervised representation learning, yet its high-dimensional theory remains largely limited to single-component recovery. We develop an asymptotically exact mean-field theory…
Independent component analysis (ICA) is a fundamental statistical tool used to reveal hidden generative processes from observed data. However, traditional ICA approaches struggle with the rotational invariance inherent in Gaussian…
A framework named Copula Component Analysis (CCA) for blind source separation is proposed as a generalization of Independent Component Analysis (ICA). It differs from ICA which assumes independence of sources that the underlying components…
The seventh part of the OGLE-III Catalog of Variable Stars (OIII-CVS) consists of 4630 classical Cepheids in the Small Magellanic Cloud (SMC). The sample includes 2626 fundamental-mode (F), 1644 first-overtone (1O), 83 second-overtone (2O),…
A central problem in unsupervised deep learning is how to find useful representations of high-dimensional data, sometimes called "disentanglement". Most approaches are heuristic and lack a proper theoretical foundation. In linear…
We present a collection of 65,981 Mira-type variable stars found in the Optical Gravitational Lensing Experiment (OGLE) project database. Two-thirds of our sample (40,356 objects) are located in the Galactic bulge fields, whereas 25,625…
Three fields covering the central part of the globular cluster Omega Cen were surveyed in a search for variable stars. We present V-band light curves for 22 periodic variables: 9 SX~Phe stars, 7 contact binaries, 5 detached or semi-detached…
Principal component analysis (PCA), along with its extensions to manifolds and outlier contaminated data, have been indispensable in computer vision and machine learning. In this work, we present a unifying formalism for PCA and its…
Independent Component Analysis (ICA) aims to recover independent latent variables from observed mixtures thereof. Causal Representation Learning (CRL) aims instead to infer causally related (thus often statistically dependent) latent…
Principal component analysis (PCA) is a commonly used pattern analysis method that maps high-dimensional data into a lower-dimensional space maximizing the data variance, that results in the promotion of separability of data. Inspired by…