Related papers: A study of supervised classification of Hipparcos …
Support Vector Machines (SVMs) are an important tool for performing classification on scattered data, where one usually has to deal with many data points in high-dimensional spaces. We propose solving SVMs in primal form using feature maps…
We investigate the line-profile variability (lpv) of H-alpha for a large sample of O-type supergiants. We used the Temporal Variance Spectrum (TVS) analysis, developed by Fullerton et al 1996 and modified by us to take into account the…
We use automated surface photometry and pattern classification techniques to morphologically classify galaxies. The two-dimensional light distribution of a galaxy is reconstructed using Fourier series fits to azimuthal profiles computed in…
Support Vector Machine (SVM) is an efficient classification approach, which finds a hyperplane to separate data from different classes. This hyperplane is determined by support vectors. In existing SVM formulations, the objective function…
Unevenly spaced time series are common in astronomy because of the day-night cycle, weather conditions, dependence on the source position in the sky, allocated telescope time, corrupt measurements, for example, or be inherent to the…
Aims: We present a custom support vector machine classification package for photometric redshift estimation, including comparisons with other methods. We also explore the efficacy of including galaxy shape information in redshift…
Context. The Vista Variables in the V\'ia L\'actea (VVV) ESO Public Survey is a variability survey of the Milky Way bulge and an adjacent section of the disk carried out from 2010 on ESO Visible and Infrared Survey Telescope for Astronomy…
Identifying spurious reduction artefacts in galaxy spectra is a challenge for large surveys. We present an algorithm for identifying and repairing residual spurious features in sky-subtracted galaxy spectra with application to the VIPERS…
Despite the important advances in space asteroseismology during the last decade, the early phases of evolution of stars with masses above $\sim$15 M$_{\odot}$ have been only vaguely explored up to now. Our goal is to detect, analyze and…
(abridged) The velocity distribution f(v) of nearby stars is estimated, via a maximum- likelihood algorithm, from the positions and tangential velocities of a kinematically unbiased sample of 14369 stars observed by the HIPPARCOS satellite.…
The goal of our study is to provide a reliable classification of variability of A-F stars brighter than 11 mag located in the northern TESS continuous viewing zone. We also aim at thorough discussion about issues in the classification…
We present the Extended Hipparcos Compilation (XHIP), a database of all stars in the New Reduction of the Hipparcos Catalog extensively cross-referenced with data from a broad survey of presently available sources. The resulting collection…
We present a comprehensive re-analysis of stellar photometric variability in the field of the open cluster M37 following the application of a new photometry and de-trending method to MMT/Megacam image archive. This new analysis allows a…
We introduce a novel algorithm that computes the $k$-sparse principal component of a positive semidefinite matrix $A$. Our algorithm is combinatorial and operates by examining a discrete set of special vectors lying in a low-dimensional…
We present a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories. We then…
This paper investigates the asymptotic behavior of the soft-margin and hard-margin support vector machine (SVM) classifiers for simultaneously high-dimensional and numerous data (large $n$ and large $p$ with $n/p\to\delta$) drawn from a…
We have studied a sample of 28 periodically variable B-type supergiants selected from the HIPPARCOS mission and 12 comparison stars covering the whole B-type spectral range. Our goal is to test if their variability is compatible with…
We have carried out a photometric survey of a complete, volume-limited sample of gamma Doradus candidates. The sample was extracted from the Hipparcos catalog and consists of 114 stars with colors and absolute magnitudes within the range of…
We propose to directly compute classification estimates by learning features encoded with their class scores using PCA. Our resulting model has a encoder-decoder structure suitable for supervised learning, it is computationally efficient…
Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification…