In this paper, we propose a novel fuzzy fusion of image residue features for detecting tampering or forgery in video sequences. We suggest use of feature selection techniques in conjunction with fuzzy fusion approach to enhance the robustness of tamper detection methods. We examine different feature selection techniques, the independent component analysis (ICA), and the canonical correlation an...

Kernel canonical correlation analysis (CCA) is a nonlinear extension of CCA, which aims at extracting information shared by two random variables. In this paper, a new notion of conditional kernel CCA is introduced. Conditional kernel CCA aims at analyzing the effect of variable Z to the dependence between X and Y . Rates of convergence of an empirical normalized conditional cross-covariance ope...

This paper illustrates how canonical correlation analysis can be used for designing efficient visual operators by learning. The approach is highly task oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of images displaying a strong dependence in the chosen feature but are otherwise independent. Experimental results are presented illus...

This paper presents an approach that incorporates canonical correlation analysis for monocular 3D face tracking as a rigid object. It also provides the comparison between the linear and the non linear version (kernel) of the CCA. The 3D pose of the face is estimated from observed raw brightness shape-free 2D image patches. A parameterized geometric face model is adopted to crop out and to norma...

We study canonical correlation analysis (CCA) as a stochastic optimization problem. We show that regularized CCA is efficiently PAC-learnable. We give stochastic approximation (SA) algorithms that are instances of stochastic mirror descent, which achieve -suboptimality in the population objective in time poly( 1 , 1 δ , d) with probability 1− δ, where d is the input dimensionality.

While kernel canonical correlation analysis (CCA) has been applied in many contexts, the convergence of finite sample estimates of the associated functions to their population counterparts has not yet been established. This paper gives a mathematical proof of the statistical convergence of kernel CCA, providing a theoretical justification for the method. The proof uses covariance operators defi...

Regularized Generalized Canonical Correlation Analysis (RGCCA) is currently geared for the analysis two-way data matrix. In this paper, multiway RGCCA (MGCCA) extends RGCCA to the multiway data configuration. More specifically, MGCCA aims at studying the complex relationships between a set of three-way data table.

In this paper, we focus on PLS-SEM’s ability to handle models with observable binary outcomes. We examine the different ways in which a binary outcome may appear in a model and distinguish those situations in which a binary outcome is indeed problematic versus those in which one can easily incorporate it into a PLS-SEM analysis. Explicating such details enables IS researchers to distinguish dif...

Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation, and is equivalent to solving a generalized eigenvalue problem. The maximal correlation coefficient (being a solution of this problem) is the first canonical correlation coef...

Canonical correlation analysis studies associations between two sets of random variables. Its standard computation is based on sample covariance matrices, which are however very sensitive to outlying observations. In this note we introduce, discuss and compare four different ways for performing a robust canonical correlation analysis. One method uses robust estimators of the involved covariance...