472,398 research outputs found
Fast robust correlation for high-dimensional data
The product moment covariance is a cornerstone of multivariate data analysis,
from which one can derive correlations, principal components, Mahalanobis
distances and many other results. Unfortunately the product moment covariance
and the corresponding Pearson correlation are very susceptible to outliers
(anomalies) in the data. Several robust measures of covariance have been
developed, but few are suitable for the ultrahigh dimensional data that are
becoming more prevalent nowadays. For that one needs methods whose computation
scales well with the dimension, are guaranteed to yield a positive semidefinite
covariance matrix, and are sufficiently robust to outliers as well as
sufficiently accurate in the statistical sense of low variability. We construct
such methods using data transformations. The resulting approach is simple, fast
and widely applicable. We study its robustness by deriving influence functions
and breakdown values, and computing the mean squared error on contaminated
data. Using these results we select a method that performs well overall. This
also allows us to construct a faster version of the DetectDeviatingCells method
(Rousseeuw and Van den Bossche, 2018) to detect cellwise outliers, that can
deal with much higher dimensions. The approach is illustrated on genomic data
with 12,000 variables and color video data with 920,000 dimensions
Fast and robust appearance-based tracking
We introduce a fast and robust subspace-based approach to appearance-based object tracking. The core of our approach is based on Fast Robust Correlation (FRC), a recently proposed technique for the robust estimation of large translational displacements. We show how the basic principles of FRC can be naturally extended to formulate a robust version of Principal Component Analysis (PCA) which can be efficiently implemented incrementally and therefore is particularly suitable for robust real-time appearance-based object tracking. Our experimental results demonstrate that the proposed approach outperforms other state-of-the-art holistic appearance-based trackers on several popular video sequences
What makes slow samples slow in the Sherrington-Kirkpatrick model
Using results of a Monte Carlo simulation of the Sherrington-Kirkpatrick
model, we try to characterize the slow disorder samples, namely we analyze
visually the correlation between the relaxation time for a given disorder
sample with several observables of the system for the same disorder sample.
For temperatures below but not too low, fast samples (small relaxation
times) are clearly correlated with a small value of the largest eigenvalue of
the coupling matrix, a large value of the site averaged local field probability
distribution at the origin, or a small value of the squared overlap is more robust) . There is a strong correlation between the values of
the relaxation time for two distinct values of the temperature, but this
correlation decreases as the system size is increased. This may indicate the
onset of temperature chaos
Bound excitons in time-dependent density-functional-theory: optical and energy-loss spectra
A robust and efficient frequency dependent and non-local exchange-correlation
is derived by imposing time-dependent density-functional
theory (TDDFT) to reproduce the many-body diagrammatic expansion of the
Bethe-Salpeter polarization function. As an illustration, we compute the
optical spectra of LiF, \sio and diamond and the finite momentum transfer
energy-loss spectrum of LiF. The TDDFT results reproduce extremely well the
excitonic effects embodied in the Bethe-Salpeter approach, both for strongly
bound and resonant excitons. We provide a working expression for that
is fast to evaluate and easy to implement.Comment: 4 pages, 2 figures. To appear in Phys. Rev. Let
Robust similarity registration technique for volumetric shapes represented by characteristic functions
This paper proposes a novel similarity registration technique for volumetric shapes implicitly represented by their characteristic functions (CFs). Here, the calculation of rotation parameters is considered as a spherical crosscorrelation problem and the solution is therefore found using the standard phase correlation technique facilitated by principal components analysis (PCA).Thus, fast Fourier transform (FFT) is employed to vastly improve efficiency and robustness. Geometric moments are then used for shape scale estimation which is independent from rotation and translation parameters. It is numericallydemonstrated that our registration method is able to handle shapes with various topologies and robust to noise and initial poses. Further validation of our method is performed by registering a lung database
Digital Ripple Correlation Control for Photovoltaic Applications
Ripple correlation control (RCC) is a fast, robust online optimization technique. RCC is particularly suited for switching power converters, where the inherent ripple provides information about the system operating point. The present work examines a digital formulation that has reduced power consumption and greater robustness. A maximum power point tracker for a photovoltaic panel demonstrates greater than 99% tracking accuracy and fast convergence
A Solution to The Similarity Registration Problem of Volumetric Shapes
This paper provides a novel solution to the volumetric similarity registration problem usually encountered in statistical study of shapes and shape-based image segmentation. Shapes are implicitly representedby characteristic functions (CFs). By mapping shapes to a spherical coordinate system, shapes to be registered are projected to unit spheres and thus, rotation and scale parameters can be conveniently calculated.Translation parameter is computed using standard phase correlation technique. The method goes through intensive tests and is shown to be fast, robust to noise and initial poses, and suitable for a variety of similarity registration problems including shapes with complex structures and various topologies
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