Dictionary-based decomposition of linear mixtures of Gaussian processes

Christophe Couvreur; Yoram Bresler

Dictionary-based decomposition of linear mixtures of Gaussian processes

Christophe Couvreur, Yoram Bresler

Research output: Contribution to journal › Conference article › peer-review

Abstract

We consider the problem of detecting and classifying an unknown number of multiple simultaneous Gaussian processes with unknown variances given a finite length observation of their sum and a dictionary of candidate models for the signals. The optimal minimum description length (MDL) detector is presented. Asymptotic and quadratic approximations of the MDL criterion are derived, and regularization algorithms for their efficient implementation are described. The performance of the algorithms is illustrated by numerical simulations. Interpretations in terms of vector quantization and in model-based spectral analysis are discussed together with applications and possible extensions.

Original language	English (US)
Pages (from-to)	2519-2522
Number of pages	4
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	5
State	Published - Jan 1 1996
Externally published	Yes
Event	Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA Duration: May 7 1996 → May 10 1996

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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abstract = "We consider the problem of detecting and classifying an unknown number of multiple simultaneous Gaussian processes with unknown variances given a finite length observation of their sum and a dictionary of candidate models for the signals. The optimal minimum description length (MDL) detector is presented. Asymptotic and quadratic approximations of the MDL criterion are derived, and regularization algorithms for their efficient implementation are described. The performance of the algorithms is illustrated by numerical simulations. Interpretations in terms of vector quantization and in model-based spectral analysis are discussed together with applications and possible extensions.",

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year = "1996",

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AU - Bresler, Yoram

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N2 - We consider the problem of detecting and classifying an unknown number of multiple simultaneous Gaussian processes with unknown variances given a finite length observation of their sum and a dictionary of candidate models for the signals. The optimal minimum description length (MDL) detector is presented. Asymptotic and quadratic approximations of the MDL criterion are derived, and regularization algorithms for their efficient implementation are described. The performance of the algorithms is illustrated by numerical simulations. Interpretations in terms of vector quantization and in model-based spectral analysis are discussed together with applications and possible extensions.

AB - We consider the problem of detecting and classifying an unknown number of multiple simultaneous Gaussian processes with unknown variances given a finite length observation of their sum and a dictionary of candidate models for the signals. The optimal minimum description length (MDL) detector is presented. Asymptotic and quadratic approximations of the MDL criterion are derived, and regularization algorithms for their efficient implementation are described. The performance of the algorithms is illustrated by numerical simulations. Interpretations in terms of vector quantization and in model-based spectral analysis are discussed together with applications and possible extensions.

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M3 - Conference article

AN - SCOPUS:0029725338

VL - 5

SP - 2519

EP - 2522

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

SN - 0736-7791

T2 - Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6)

Y2 - 7 May 1996 through 10 May 1996

ER -

Dictionary-based decomposition of linear mixtures of Gaussian processes

Abstract

ASJC Scopus subject areas

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