Binary Independent Component Analysis With or Mixtures

Independent Component Analysis, Contrasts, and Convolutive Mixtures

The paper is organized as follows. Section 1 states the problem, assumptions, and notations, and includes a brief partial bibliographical survey. Section 2 is a reminder about whitening operations and cumulants. Section 3 defines a wide class of contrast functionals and gives several examples; some of them are new. Practical algorithms are presented in section 4. The effect of a carrier frequen...

​Rank based Least-squares Independent Component Analysis

  In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...

Implementing Decisions in Binary Decision Trees Using Independent Component Analysis

There are various ways to implement decisions in binary decision trees. Most approaches can be interpreted as greedy methods optimizing some local goodness criteria at each node. Often it is required that either target values (regression trees) or class labels (classi-cation trees) are available. In this paper linear ICA is applied to implement the decisions in a binary tree. The rationale is t...

Independent Component Analysis for Binary Data: an Experimental Study

Binary Principal Component Analysis

Efficient and compact representation of images is a fundamental problem in computer vision. Principal Component Analysis (PCA) has been widely used for image representation and has been successfully applied to many computer vision algorithms. In this paper, we propose a method that uses Haar-like binary box functions to span a subspace which approximates the PCA subspace. The proposed method ca...