Most of the behaviors people exhibit while being part of a crowd are social processes that tend to emerge among groups and as a consequence, detecting groups in crowds is becoming an important issue in modern behavior analysis. We propose a supervised correlation clustering technique that employs Structural SVM and a proxemic based feature to learn how to partition people trajectories in groups...

Abstract. We use the conjecture of Conrey, Farmer and Zirnbauer for averages of ratios of the Riemann zeta function [10] to calculate all the lower order terms of the triple correlation function of the Riemann zeros. A previous approach was suggested by Bogomolny and Keating [6] taking inspiration from semi-classical methods. At that point they did not write out the answer explicitly, so we do ...

In this paper we describe a method for functional connectivity analysis of fMRI data between given brain regions-of-interest (ROIs). The method relies on nonnegativity constrained- and spatially regularized multiset canonical correlation analysis (CCA), and assigns weights to the fMRI signals of the ROIs so that their representative signals become simultaneously maximally correlated. The differ...

Suggests org.Hs.eg.db, org.Mm.eg.db, infotheo, entropy, minet ZipData no License GPL (>= 2) Description Functions necessary to perform Weighted Correlation Network Analysis on highdimensional data. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a n...

We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn complex nonlinear transformations of two views of data such that the resulting representations are highly linearly correlated. Parameters of both transformations are jointly learned to maximize the (regularized) total correlation. It can be viewed as a nonlinear extension of the linear method canonical correlation analys...

Canonical correlation analysis (CCA) is a classical method for seeking correlations between two multivariate data sets. During the last ten years, it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications. We review recent developments in Bayesian models and inference methods for CCA which are attract...

Correlation analysis is one of the key elements of statistics, and has various applications in data analysis. Whereas most existing measures can only detect pairwise correlations between two dimensions, modern analysis aims at detecting correlations in multi-dimensional spaces. We propose MAC, a novel multivariate correlation measure designed for discovering multidimensional patterns. It belong...

Hervé Abdi, Vincent Guillemot, Aida Eslami and Derek Beaton School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA Bioinformatics and Biostatistics Hub, Institut Pasteur (IP), C3BI, USR 3756 CNRS, Paris, France Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada Rotman Research Institute, Baycrest Health Sciences, Toro...

In service-oriented computing, correlations are used to determine links between service providers and users. A correlation contains values for some variables received in a communication. Subsequent messages will only be received when they match the values of the correlation. Correlations allow for the implementation of sessions, local shared memory, gradually provided input, or input provided i...

The banking structure-performance relationship has been the subject of many studies (Heggestad, 1979). This paper addresses two problems associated with previous research through analysis of the structure-performance relationship in the savings and loan association industry. One problem is that most studies estimate the structure-performance relationship with multiple regression analysis. The p...