Signal Processing - Special issue: Genomic signal processing
Combined image compression and denoising using wavelets
Image Communication
Clustering time series gene expression data based on sum-of-exponentials fitting
EURASIP Journal on Applied Signal Processing
Improved spatially adaptive MDL denoising of images using normalized maximum likelihood density
Image and Vision Computing
CoCo: coding cost for parameter-free outlier detection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Signal Processing
Universal models for the exponential distribution
IEEE Transactions on Information Theory
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Model selection by sequentially normalized least squares
Journal of Multivariate Analysis
A minimal description length scheme for polynomial regression
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Noise level estimation using haar wavelet packet trees for sensor robust outlier detection
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
A novel criterion for characterizing diffusion anisotropy in HARDI data based on the MDL technique
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Description length and dimensionality reduction in functional data analysis
Computational Statistics & Data Analysis
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`The so-called denoising problem, relative to normal models for noise, is formalized such that “noise” is defined as the incompressible part in the data while the compressible part defines the meaningful information-bearing signal. Such a decomposition is effected by minimization of the ideal code length, called for by the minimum description length (MDL) principle, and obtained by an application of the normalized maximum-likelihood technique to the primary parameters, their range, and their number. For any orthonormal regression matrix, such as defined by wavelet transforms, the minimization can be done with a threshold for the squared coefficients resulting from the expansion of the data sequence in the basis vectors defined by the matrix