Related papers: Principal Component Analysis to correct data syste…

Outliers contaminating data sets are a challenge to statistical estimators. Even a small fraction of outlying observations can heavily influence most classical statistical methods. In this paper we propose generalized spherical principal…

We present two diagnostic methods based on ideas of Principal Component Analysis and demonstrate their efficiency for sophisticated processing of multicolour photometric observations of variable objects.

Within the framework of functional data analysis, we develop principal component analysis for periodically correlated time series of functions. We define the components of the above analysis including periodic, operator-valued filters,…

We propose a new information theoretic metric for finding periodicities in stellar light curves. Light curves are astronomical time series of brightness over time, and are characterized as being noisy and unevenly sampled. The proposed…

Research into light curves from stars (temporal variation of brightness) has completely changed how exoplanets are discovered or characterised. This study including star light curves from the Kepler dataset as a way to discover exoplanets…

We investigate the impact of instrumental systematic errors on the potential of cosmic microwave background polarization experiments targeting primordial B-modes. To do so, we introduce spin-weighted Muller matrix-valued fields describing…

Methodologies for multidimensionality reduction aim at discovering low-dimensional manifolds where data ranges. Principal Component Analysis (PCA) is very effective if data have linear structure. But fails in identifying a possible…

Principal Component Analysis (PCA) is one of the most important methods to handle high dimensional data. However, most of the studies on PCA aim to minimize the loss after projection, which usually measures the Euclidean distance, though in…

Stellar rotation plays a key role in stellar activity. The rotation period could be detected through light curve variations caused by starspots. Kepler provides two types of light curves, one is the Pre-search Data Conditioning (PDC) light…

Dimension reduction for high-dimensional compositional data plays an important role in many fields, where the principal component analysis of the basis covariance matrix is of scientific interest. In practice, however, the basis variables…

While the Kepler Mission was designed to look at tens of thousands of faint stars (V > 12), brighter stars that saturated the detector are important because they can be and have been observed very accurately by other instruments. By…

In computer vision most iterative optimization algorithms, both sparse and dense, rely on a coarse and reliable dense initialization to bootstrap their optimization procedure. For example, dense optical flow algorithms profit massively in…

The K2 mission will make use of the Kepler spacecraft and its assets to expand upon Kepler's groundbreaking discoveries in the fields of exoplanets and astrophysics through new and exciting observations. K2 will use an innovative way of…

Archives of long photometric surveys, like the Kepler database, are a gold mine for studying flares. However, identifying them is a complex task; while in the case of single-target observations it can be easily done manually by visual…

Principal Component Analysis (PCA)-based techniques can separate data into different uncorrelated components and facilitate the statistical analysis as a pre-processing step. Independent Component Analysis (ICA) can separate statistically…

Principal Component Analysis (PCA) is a well-known multivariate technique used to decorrelate a set of vectors. PCA has been extensively applied in the past to the classification of stellar and galaxy spectra. Here we apply PCA to the…

Principal Component Analysis (PCA) is one of the most used tools for extracting low-dimensional representations of data, in particular for time series. Performances are known to strongly depend on the quality (amount of noise) and the…

We present the results of an automated variability analysis of the Kepler public data measured in the first quarter (Q1) of the mission. In total, about 150 000 light curves have been analysed to detect stellar variability, and to identify…

We apply a PCA-based pre-whitening method to the entire collection of main Kepler mission long-cadence data for KIC 8462852 spanning four years. This technique removes the correlated variations of instrumental origin in both the detected…

Recurrence plots were introduced to help aid the detection of signals in complicated data series. This effort was furthered by the quantification of recurrence plot elements. We now demonstrate the utility of combining recurrence…