Sung-Phil Kim, Yadunandana N. Rao, Deniz Erdogmus, Justin C. Sanchez, Jose C. Principe
We propose the use of nonnegative matrix factorization (NMF) as a model-independent methodology to analyze neural activity. We demonstrate that, using this technique, it is possible to identify local...
Sung-Phil Kim, Yadunandana N. Rao, Deniz Erdogmus, Justin C. Sanchez, Jose C. Principe
We propose the use of nonnegative matrix factorization (NMF) as a model-independent methodology to analyze neural activity. We demonstrate that, using this technique, it is possible to identify local...
Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation (2004)
Deniz Erdogmus, Yadunandana N. Rao, Hemanth Peddaneni, Anant Hegde, Jose C. Principe
Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these...
Fast RLS-like algorithm for generalized eigendecomposition and its applications (2004)
Yadunandana N. Rao, C. Principe, Tan F. Wong
Abstract. Generalized eigendecomposition (GED) plays a vital role in many signal-processing applications. In this paper, we will propose a new method for computing the generalized eigenvectors, which...
Minimax Mutual Information Approach for Independent Component Analysis (2004)
Deniz Erdogmus, Yadunandana N. Rao, Jose C. Principe
Minimum output mutual information is regarded as a natural criterion for independent component analysis (ICA) and is used as the performance measure in many ICA algorithms. Two common approaches in...
Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation (2004)
Jose C. Principe, Anant Hegde, Hemanth Peddaneni, Yadunandana N. Rao, Deniz Erdogmus
Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these...
Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation (2004)
Deniz Erdogmus, Yadunandana N. Rao, Hemanth Peddaneni, Anant Hegde, Jose C. Principe
Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these...
Deniz Erdogmus, Yadunandana N. Rao, Jose C. Principe
SIPEX-G is a fast-converging, robust, gradient-based PCA algorithm that has been recently proposed by the authors. Its superior performance in synthetic and real data compared with its benchmark...
Jose C. Principe, Yadunandana N. Rao, Deniz Erdogmus
SIPEX-G is a fast-converging, robust, gradient-based PCA algorithm that has been recently proposed by the authors. Its superior performance in synthetic and real data compared with its benchmark...
Deniz Erdogmus, Yadunandana N. Rao, Jose C. Principe
SIPEX-G is a fast-converging, robust, gradient-based PCA algorithm that has been recently proposed by the authors. Its superior performance in synthetic and real data compared with its benchmark...