Related papers: Principal component analysis-based inversion of ef…
The infrared flux method (IRFM) has been applied to a sample of 135 dwarf and 36 giant stars covering the following regions of the atmospheric parameters space: 1) the metal-rich ([Fe/H]>0) end (consisting mostly of planet-hosting stars),…
We introduce a new Procrustes-type method called matching component analysis to isolate components in data for transfer learning. Our theoretical results describe the sample complexity of this method, and we demonstrate through numerical…
Principal component analysis continues to be a powerful tool in dimension reduction of high dimensional data. We assume a variance-diverging model and use the high-dimension, low-sample-size asymptotics to show that even though the…
This paper presents a novel approach to functional principal component analysis (FPCA) in Bayes spaces in the setting where densities are the object of analysis, but only few individual samples from each density are observed. We use the…
This paper focuses on the utility of various data transformation techniques, which might be under the principal component analysis (PCA) category, on exoplanet research. The first section introduces the methodological background of PCA and…
Within the Ginzburg-Landau functional framework for the superconducting transition, we analyze the fluctuation-driven shift of the critical temperature. In addition to the order parameter fluctuations, we also take into account the…
We combine results from interferometry, asteroseismology and spectroscopy to determine accurate fundamental parameters of 23 bright solar-type stars, from spectral type F5 to K2 and luminosity classes III to V. For some stars we can use…
This paper introduces the use of Principal Component Analysis as a method to decompose the waveform catalogues to produce a set of orthonormal basis vectors. We apply this method to a set of supernova waveforms and compare the basis vectors…
In this paper, we analyze the applicability of the principal component analysis (PCA) as a tool to extract the Sq variation of the geomagnetic field. We tested different geomagnetic field components and used data measured at different…
In this work, we propose a novel centrality metric, referred to as star centrality, which incorporates information from the closed neighborhood of a node, rather than solely from the node itself, when calculating its topological importance.…
In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant…
Some selected catalogs of the effective temperatures for F, G and K stars are analyzed. By an improved technique we estimate the external errors of these catalogs from data intercomparisons. The values of the effective temperatures are then…
A new integration scheme, combining the stability and the precision of usual pseudo-spectral codes with the locality of finite differences methods, is introduced. It turns out to be particularly suitable for the study of front and…
Some issues of inverting asteroseismic frequency data are discussed, including the use of model calibration and linearized inversion. An illustrative inversion of artificial data for solar-type stars, using least-squares fitting of a small…
Principal Geodesic Analysis (PGA) is applied to a climate time series. First, we transform each multidimensional sequence into the path signature. Since the signature lives in a curved space, usual principal component analysis (PCA) is not…
We introduce a temperature-dependent parameterization in the modified embedded-atom method and combine it with molecular dynamics to simulate the diverse physical properties of the \delta - and \epsilon - phases of elemental plutonium. The…
External errors of effective temperatures of stars for selected libraries are estimated from data intercomparisons. It is found that the obtained errors are mainly in a good correspondence with the published data. The results may be used to…
This paper reports on the application of the supervised machine-learning algorithm to the stellar effective temperature regression for the second $Gaia$ data release, based on the combination of the stars in four spectroscopic surveys:…
We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in high-dimensional data modalities such as optical image…
Principal component analysis (PCA) is a widespread technique for data analysis that relies on the covariance-correlation matrix of the analyzed data. However to properly work with high-dimensional data, PCA poses severe mathematical…