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This paper proposes a robust high-dimensional sparse canonical correlation analysis (CCA) method for investigating linear relationships between two high-dimensional random vectors, focusing on elliptical symmetric distributions. Traditional…

Methodology · Statistics 2025-04-18 Chengde Qian , Yanhong Liu , Long Feng

We present a theoretical study of frequency correlations of light backscattered from a random scattering medium. This statistical quantity provides insight into the dynamics of multiple scattering processes accessible both, in theoretical…

Optics · Physics 2015-06-23 Angelika Knothe , Thomas Wellens

Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an $\ell_2$ penalty on the CCA…

Methodology · Statistics 2021-07-30 Elena Tuzhilina , Leonardo Tozzi , Trevor Hastie

We present the results of a numerical study based on the analysis of the MUSIC-2 simulations, aimed at estimating the expected concentration-mass relation for the CLASH cluster sample. We study nearly 1400 halos simulated at high spatial…

Canonical Correlation Analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to…

Methodology · Statistics 2015-12-22 Jacob Coleman , Joseph Replogle , Gabriel Chandler , Johanna Hardin

Coherent X-ray Diffraction Imaging (CXDI) technique offers unique insights into the nanoscale world, enabling the reconstruction of 3D structures with a nanoscale resolution achieved through computational phase reconstruction from measured…

Computational Physics · Physics 2024-03-26 Fangzhou Ai , Oleg Shpyrko , Vitaliy Lomakin

Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. Several variants of CCA have been introduced in the literature, in particular, variants based…

Machine Learning · Computer Science 2022-03-25 Tomer Friedlander , Lior Wolf

Deformable image registration is a fundamental requirement for medical image analysis. Recently, transformers have been widely used in deep learning-based registration methods for their ability to capture long-range dependency via…

Image and Video Processing · Electrical Eng. & Systems 2024-12-25 Mingyuan Meng , Michael Fulham , Lei Bi , Jinman Kim

The evolution from 3rd to 4th generation synchrotron radiation (SR) sources significantly enhanced their coherence, making coherent scattering techniques such as coherent X-ray diffraction imaging (CXDI) and X-ray photon correlation…

Accelerator Physics · Physics 2021-10-20 Han Xu , Zhongzhu Zhu , Peng Liu , Yuhui Dong , Liang Zhou

Diffuse scattering of light from disordered assemblies is traditionally viewed as an uncontrollable broadband scattering background resulting in whitish hues. Here, we demonstrate that correlated disorder enables precise engineering of…

We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The…

Machine Learning · Computer Science 2020-05-26 Hok Shing Wong , Li Wang , Raymond Chan , Tieyong Zeng

Diffraction of coherent x-ray beams is treated through the Fractionnal Fourier transform. The transformation allow us to deal with coherent diffraction experiments from the Fresnel to the Fraunhofer regime. The analogy with the…

Other Condensed Matter · Physics 2025-01-08 David Le Bolloc'h , Jean-Francois Sadoc

The challenge of obtaining galaxy cluster masses is increasingly being addressed by multiwavelength measurements. As scatters in measured cluster masses are often sourced by properties of or around the clusters themselves, correlations…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-04 Yookyung Noh , J. D. Cohn

We examine the Detrended Fluctuation Analysis (DFA), which is a well-established method for the detection of long-range correlations in time series. We show that deviations from scaling that appear at small time scales become stronger in…

Statistical Mechanics · Physics 2009-11-07 Jan W. Kantelhardt , Eva Koscielny-Bunde , Henio H. A. Rego , Shlomo Havlin , Armin Bunde

A growing number of shock compression experiments, especially those involving laser compression, are taking advantage of in situ x-ray diffraction as a tool to interrogate structure and microstructure evolution. Although these experiments…

Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, however, it is limited…

Machine Learning · Statistics 2015-02-10 Yong Luo , Dacheng Tao , Yonggang Wen , Kotagiri Ramamohanarao , Chao Xu

The Fourier inversion of phased coherent diffraction patterns offers images without the resolution and depth-of-focus limitations of lens-based tomographic systems. We report on our recent experimental images inverted using recent…

We study the effect of uncorrelated random disorder on the temperature dependence of the superfluid stiffness in the two-dimensional classical XY model. By means of a perturbative expansion in the disorder potential, equivalent to the…

Superconductivity · Physics 2019-03-27 Ilaria Maccari , Lara Benfatto , Claudio Castellani

X-ray photon correlation spectroscopy (XPCS) allows for the resolution of dynamic processes within a material across a wide range of length and time scales. X-ray speckle visibility spectroscopy (XSVS) is a related method that uses a single…

Numerical Analysis · Mathematics 2023-01-09 Shaswat Mohanty , Christopher B. Cooper , Hui Wang , Mengning Liang , Wei Cai

How does one find dimensions in multivariate data that are reliably expressed across repetitions? For example, in a brain imaging study one may want to identify combinations of neural signals that are reliably expressed across multiple…

Machine Learning · Statistics 2022-12-05 Lucas C. Parra , Stefan Haufe , Jacek P. Dmochowski