A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons...
We develop a new component analysis framework, the Noisy-Or Component Analyzer (NOCA), that targets high-dimensional binary data. NOCA is a probabilistic latent variable model tha...
Unsupervised or Self-Organized learning algorithms have become very popular for discovery of significant patterns or features in the input data. The three prominent algorithms name...
This paper investigates the use of range images of faces for recognizing people. 3D scans of faces lead to range images that are linearly projected to low-dimensional subspaces fo...
In this paper, it is shown that Independent Component Analysis (ICA) of sparse signals (sparse ICA) can be seen as a cluster-wise Principal Component Analysis (PCA). Consequently,...
Massoud Babaie-Zadeh, Christian Jutten, Ali Mansou...
For software and more illustrations: http://www.psi.utoronto.ca/anitha/fastTCA.htm Dimensionality reduction techniques such as principal component analysis and factor analysis are...
An eigenvalue method is developed for analyzing periodic structure in speech. Signals are analyzed by a matrix diagonalization reminiscent of methods for principal component analy...
Blind source separation (BSS) has become one of the major signal and image processing area in many applications. Principal component analysis (PCA) and Independent component analys...
Conventional remote sensing classification techniques that model the data in each class with a multivariate Gaussian distribution are inefficient, as this assumption is generally ...
Chintan A. Shah, Manoj K. Arora, Stefan A. Robila,...
Abstract. Multidimensional data projection and visualisation are becoming increasingly important and have found wide applications in many fields such as decision support, bioinform...