MOTIVATION Identification and characterization of protein structure regularities can reveal the mechanisms governing protein structure, function and evolution. Here we focus on an intermediate level of regularity. We have developed automated methods to systematically construct a dictionary of supersecondary structures that can be used as 'protein parts' to describe fold-sized structures. RESU...

SHORT ABSTRACT: The paper is a contribution to the problem of producing sensor-specific seabed maps, that describe the morphological features of each different region in the area considered. It applies the Minimum Description Length (MDL) principle to identify the dimensionality of a statistical shape model that describes them. Our shape model combines the finite-dimensional representation of c...

Autonomous model building is a crucial trend in model based methods like AAMs. This paper introduces an approach that deals with non-linearities by detecting distinct sub-parts in the data. Sub-models each representing an individual sub-part are derived from a minimum description length criterion. Thereby the resulting clique of models is more compact and obtains a better generalization behavio...

The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our Subdue substructure discovery system based on the minimum description length principle. The Subdue system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previo...

An autoencoder network uses a set of recognition weights to convert an input vector into a code vector. It then uses a set of generative weights to convert the code vector into an approximate reconstruction of the input vector. We derive an objective function for training autoencoders based on the Minimum Description Length (MDL) principle. The aim is to minimize the information required to des...

It is shown that the two-part Minimum Description Length Principle can be used to discriminate among different models that can explain a given observed dataset. The description length is chosen to be the sum of the lengths of the message needed to encode the model plus the message needed to encode the data when the model is applied to the dataset. It is verified that the proposed principle can ...

One of the most important issues for computational methods is their time complexity. This paper introduces a temporal MDL (minimum description length) policy for evolving neural networks based on their execution time on the hosting hardware. Temporal MDL implements an adaptive selection pressure based on the actual processing time of the evolving solutions and thus favors creation of faster, mo...

Active shape models are a powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving subparts in the data can deteriorate the resulting model considerably. Although non-line...

Denoising has always been theoretically considered as removal of high frequency disturbances having Gaussian distribution. Here We relax this assumption and the method used here is completely different from traditional thresholding schemes. The data are converted to wavelet coefficients, a part of which represents the denoised signal and the remaining part the noise. The coefficients are distri...

Probabilistic networks can be constructed from a database of cases by selecting a network that has highest quality with respect to this database according to a given measure. A new measure is presented for this purpose based on a minimum description length (MDL) approach. This measure is compared with a commonly used measure based on a Bayesian approach both from a theoretical and an experiment...