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Canonical correlation analysis (CCA) is a standard tool for studying associations between two data sources; however, it is not designed for data with count or proportion measurement types. In addition, while CCA uncovers common signals, it…

Computation · Statistics 2022-08-02 Dongbang Yuan , Yunfeng Zhang , Shuai Guo , Wenyi Wang , Irina Gaynanova

We prove that the Weihrauch lattice can be transformed into a Brouwer algebra by the consecutive application of two closure operators in the appropriate order: first completion and then parallelization. The closure operator of completion is…

Logic · Mathematics 2021-07-01 Vasco Brattka , Guido Gherardi

We show that quantum oracles provide an advantage over classical oracles for answering classical counterfactual questions in causal models, or equivalently, for identifying unknown causal parameters such as distributions over functional…

Quantum Physics · Physics 2025-12-16 Ciarán M. Gilligan-Lee , Yìlè Yīng , Jonathan Richens , David Schmid

Canonical correlation analysis (CCA) is a multivariate statistical technique for finding the linear relationship between two sets of variables. The kernel generalization of CCA named kernel CCA has been proposed to find nonlinear relations…

Machine Learning · Statistics 2017-01-17 Xiaowei Zhang , Delin Chu , Li-Zhi Liao , Michael K. Ng

Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its…

Computation and Language · Computer Science 2016-07-28 Dominique Osborne , Shashi Narayan , Shay B. Cohen

Recently, a chaos-based image encryption algorithm using alternate structure (IEAS) was proposed. This paper focuses on differential cryptanalysis of the algorithm and finds that some properties of IEAS can support a differential attack to…

Cryptography and Security · Computer Science 2011-12-22 Yu Zhang , Chengqing Li , Kwok-Wo Wong , Shi Shu , Guanrong Chen

Canonical correlation analysis is a statistical technique that is used to find relations between two sets of variables. An important extension in pattern analysis is to consider more than two sets of variables. This problem can be expressed…

Machine Learning · Computer Science 2013-02-06 Jan Rupnik , Primoz Skraba , John Shawe-Taylor , Sabrina Guettes

Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. They are frequently used in problems involving latent discrete…

Generalized Canonical Correlation Analysis (GCCA) is an important tool that finds numerous applications in data mining, machine learning, and artificial intelligence. It aims at finding `common' random variables that are strongly correlated…

Machine Learning · Computer Science 2021-05-19 Mikael Sørensen , Charilaos I. Kanatsoulis , Nicholas D. Sidiropoulos

In this paper we establish a connection between ribbon graphs and Brauer graphs. As a result, we show that a compact oriented surface with marked points gives rise to a unique Brauer graph algebra up to derived equivalence. In the case of a…

Representation Theory · Mathematics 2020-12-21 Bethany Marsh , Sibylle Schroll

These lecture notes cover a brief introduction into some of the algebro-geometric techniques used in the construction of BPS algebras. The first section introduces the derived category of coherent sheaves as a useful model of branes in…

High Energy Physics - Theory · Physics 2021-12-30 Miroslav Rapcak

We give an information-theoretic interpretation of Canonical Correlation Analysis (CCA) via (relaxed) Wyner's common information. CCA permits to extract from two high-dimensional data sets low-dimensional descriptions (features) that…

Information Theory · Computer Science 2020-03-02 Michael Gastpar , Erixhen Sula

Canonical correlation analysis (CCA) is a fundamental statistical tool for exploring the correlation structure between two sets of random variables. In this paper, motivated by recent success of applying CCA to learn low dimensional…

Statistics Theory · Mathematics 2018-01-23 Zhuang Ma , Xiaodong Li

The walled Brauer algebras $B_N(m,n)$ govern Schur--Weyl duality for unitary groups $U(N)$ acting on mixed tensor spaces $V_N^{\otimes m}\otimes \overline{V}_N^{\otimes n}$ and play an important role in applications ranging from AdS/CFT to…

High Energy Physics - Theory · Physics 2026-05-01 Sanjaye Ramgoolam , Michał Studziński

The Algebraic Eraser (AE) is a cryptographic primitive that can be used to obscure information in certain algebraic cryptosystems. The Colored Burau Key Agreement Protocol (CBKAP), which is built on the AE, was introduced by I. Anshel, M.…

Cryptography and Security · Computer Science 2011-05-06 Paul E. Gunnells

A new class of associative algebras referred to as affine walled Brauer algebras are introduced. These algebras are free with infinite rank over a commutative ring containing 1. Then level two walled Brauer algebras over C are defined,…

Representation Theory · Mathematics 2013-05-03 Hebing Rui , Yucai Su

We consider a linear autoencoder in which the latent variables are quantized, or corrupted by noise, and the constraint is Schur-concave in the set of latent variances. Although finding the optimal encoder/decoder pair for this setup is a…

Information Theory · Computer Science 2021-06-09 Sourbh Bhadane , Aaron B. Wagner , Jayadev Acharya

We determine the decomposition numbers for the Brauer and walled Brauer algebra in characteristic zero in terms of certain polynomials associated to cap and curl diagrams (recovering a result of Martin in the Brauer case). We consider a…

Representation Theory · Mathematics 2010-09-22 Anton Cox , Maud De Visscher

Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly. In the example of neuroscience, studies with thousands of subjects are becoming more common, which provide…

The canonical correlation analysis (CCA) is commonly used to analyze data sets with paired data, e.g. measurements of gene expression and metabolomic intensities of the same experiments. This allows to find interesting relationships between…