Tj Tjalling Tjalkens

Context weighting for general finite-context sources

Context weighting procedures are presented for sources with models (structures) in four different classes. Although the procedures are designed for universal data compression purposes, their generality allows application in the area of classification.

Variable to fixed-length codes for Markov sources

Petrys efficient and optimal variable to fixed-length source code for discrete memoryless sources was described by Schalkwijk. By extending this coding technique we are able to give an algorithm for Markov sources that is easy to implement. We can bound the loss of efficiency as a function of the code complexity and the mismatch between the source and the code. Rates arbitrarily close to the source entropy are shown to be achievable. In this sense the codes introduced are optimal.

A universal variable-to-fixed length source code based on Lawrence's algorithm

It is shown that the modified Lawrence algorithm is universal over the class of binary memoryless sources and that the rate converges asymptotically optimally fast to the source entropy. It is proven that no codes exist that have a better asymptotic performance. The asymptotic bounds show that universal variable-to-fixed-length codes can have a significantly lower redundancy than universal fixed-to-variable-length codes with the same number of codewords. >

A context-tree weighting method for text generating sources

Summary form only given. The authors discuss context tree weighting (Willems et al. 1995). This was originally introduced as a sequential universal source coding method for the class of binary tree sources. The paper discusses the application of the method to the compaction of ASCII sequences. The estimation of redundancy and model redundancy are also considered.

The complexity of minimum redundancy coding

An efficient implementation of a Huffman code is based on the Shannon-Fano construction. An important question is: how complex is such an implementation? In the past authors have considered this question assuming an ordered source symbol alphabet. For of the compression of blocks of binary symbols this ordering must be performed explicitly and it turns out to be the complexity bottleneck.

Contrast-ultrasound dispersion imaging of cancer neovascularization by mutual-information analysis

Being an established marker for cancer growth, neovascularization is probed by several approaches with the aim of cancer imaging. Recently, analysis of the dispersion kinetics of ultrasound contrast agents (UCAs) has been proposed as a promising approach for localizing neovascularization in prostate cancer. Determined by multipath trajectories through the microvasculature, dispersion enables characterization of the microvascular architecture and, therefore, localization of cancer neovascularization. Analysis of the spatiotemporal similarity among indicator dilution curves (IDCs) measured at each pixel by dynamic contrast-enhanced ultrasound imaging has been proposed to assess the local dispersion kinetics of UCAs. Only linear similarity measures, such as temporal correlation or spectral coherence, have been used up until now. Here we investigate the use of nonlinear similarity measures by estimation of the statistical dependency between IDCs. In particular, dispersion maps are generated by estimation of the mutual information between IDCs. The method is tested for prostate cancer localization and the results compared with the histology results in 15 patients referred for radical prostatectomy because of biopsy-proven prostate cancer. With sensitivity and specificity equal to 84% and 85%, respectively, and receiver operating characteristic curve area equal to 0.92, our results outperformed those obtained by any other parameter, motivating further validation with a larger dataset and with other types of cancer.

Implementation cost of the Huffman-Shannon-Fano code

An efficient implementation of a Huffman code can be based on the Shannon-Fano construction. An important question is exactly how complex is such an implementation. In the past authors have considered this question assuming an ordered source symbol alphabet. In the case of the compression of blocks of binary symbols this ordering must be performed explicitly and it turns out to be the complexity bottleneck.

Reducing the complexity of the context-tree weighting method

The storage complexity of the CTW-method is decreased by combining the estimated probability of a node in the context tree and the weighted probabilities of its children in a single ratio.

Using Feature-Based Models with Complexity Penalization for Selecting Features

Feature selection and inference through modeling are combined into one method based on a network that can be used to point out irrelevant, redundant and dependent features in the data. It is shown that this network method is efficient in terms of reducing the number of calculations for estimating the probabilities under different model assumptions by breaking the data into fractions. We prove that the probability estimations within the network method lead to the detection of non-informative features with probability one if the data is sufficiently large. The proposed method’s accuracy in detecting complex relations between features, selecting informative features and classifying data-sets with different dimensions is assessed through experiments using both synthetic and real data. The results from the network method compare favorably with those from the well-known and powerful feature selection algorithms. It is further shown that the network method can handle complex relations between the features that are intractable for other algorithms.

An Efficient Method for Computing a Bayesian Mixture of Feature-Based Models

We describe a computationally efficient method to produce a specific Bayesian mixture of all the models in a finite set of feature-based models that assign a probability to the observed data set. Special attention is given to the bound on the regret of using the mixture instead of the best model in the set. It is proven theoretically and verified through synthetic data that this bound is relatively tight. Comparing the workload of the proposed method with the direct implementation of the Bayesian mixture shows an almost exponential improvement of computing time.