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- Petr Tichavsky, Zbynek Koldovsky
- IEEE Signal Processing Letters
- 2004

In this letter, the problem of optimal pairing of signal components separated by blind techniques in different time-windows or in different frequency bins is addressed. The optimum pairing is defined as the one which minimizes the sum of some distances (criteria of dissimilarity) of the to-be-assigned signal components. It is shown that the optimal pairing… (More)

- Z. Koldovsky, P. Tichavsky, E. Oja
- IEEE Transactions on Neural Networks
- 2006

FastICA is one of the most popular algorithms for independent component analysis (ICA), demixing a set of statistically independent sources that have been mixed linearly. A key question is how accurate the method is for finite data samples. We propose an improved version of the FastICA algorithm which is asymptotically efficient, i.e., its accuracy given by… (More)

- Anh Huy Phan, Petr Tichavsky, Andrzej Cichocki
- 2011 IEEE Statistical Signal Processing Workshop…
- 2011

Algorithms based on alternating optimization for nonnegative Tucker decompositions (NTD) such as ALS, multiplicative least squares, HALS have been confirmed effective and efficient. However, those algorithms often converge very slowly. To this end, we propose a novel algorithm for NTD using the Levenberg-Marquardt technique with fast computation method to… (More)

- Zbynek Koldovsky, Petr Tichavsky, Anh Huy Phan
- 2011 IEEE Statistical Signal Processing Workshop…
- 2011

INDSCAL is a special case of the CANDECOMP-PARAFAC (CP) decomposition of three or more-way tensors, where two factor matrices are equal. This paper provides a stability analysis of INDSCAL that is done by deriving the Cramér-Rao lower bound (CRLB) on variance of an unbiased estimate of the tensor parameters from its noisy observation (the tensor plus… (More)

The mutual information is useful measure of a random vector component dependence. It is important in many technical applications. The estimation methods are often based on the well known relation between the mutual information and the appropriate entropies. In 1999 Darbellay and Vajda [3] proposed a direct estimation methods. In this paper we compare some… (More)

- Petr Tichavský
- 2012

Conditional probability tables (CPTs) of threshold functions represent a generalization of two popular models – noisy-or and noisy-and. They constitute an alternative to these two models in case they are too rough. When using the standard inference techniques the inference complexity is exponential with respect to the number of parents of a variable. In… (More)

Tensor diagonalization means transforming a given tensor to an exactly or nearly diagonal form through multiplying the tensor by non-orthogonal invertible matrices along selected dimensions of the tensor. It is generalization of approximate joint diagonalization (AJD) of a set of matrices. In particular, we derive (1) a new algorithm for symmetric AJD,… (More)

- Naveen Dubey, Rajesh Mehra, Emmanuel Vincent, Nancy Bertin, Remi Gribonval, Frederic Bimbot +4 others
- 2016

Algorithms for Blind Audio Source Separation (BASS) in time domain can be categories as based on complete decomposition or based on complete decomposition. Partial decomposition of observation space leads to additional computational complexity and burden, to minimize resource requirement complete decomposition technique is preferred. In this script an… (More)

- Anh-Huy Phan, Petr Tichavsky, Andrzej Cichocki
- IEEE Transactions on Signal Processing
- 2015

CANDECOMP/PARAFAC (CP) approximates multiway data by sum of rank-1 tensors. Unlike matrix decomposition, the procedure which estimates the best rank- R tensor approximation through R sequential best rank-1 approximations does not work for tensors, because the deflation does not always reduce the tensor rank. In this paper, we propose a novel deflation… (More)

- Anh-Huy Phan, Petr Tichavsky, Andrzej Cichocki
- IEEE Transactions on Signal Processing
- 2015

In Part I of the study of the tensor deflation for CANDECOMP/PARAFAC, we have shown that the rank-1 tensor deflation is applicable under some conditions. Part II of the study presents several initialization algorithms suitable for the algorithm proposed in Part I. In addition, Part II contains an algorithm for the case when one or more factor matrices in… (More)