Expanding discriminative dimensions for analysis and imaging

Learning Discriminative Feature Transforms to Low Dimensions in Low Dimensions

The marriage of Renyi entropy with Parzen density estimation has been shown to be a viable tool in learning discriminative feature transforms. However, it suffers from computational complexity proportional to the square of the number of samples in the training data. This sets a practical limit to using large databases. We suggest immediate divorce of the two methods and remarriage of Renyi entr...

Discriminative bag-of-cells for imaging-genomics.

Connecting genotypes to image phenotypes is crucial for a comprehensive understanding of cancer. To learn such connections, new machine learning approaches must be developed for the better integration of imaging and genomic data. Here we propose a novel approach called Discriminative Bag-of-Cells (DBC) for predicting genomic markers using imaging features, which addresses the challenge of summa...

Expanding Dimensions Of Test Management For Object Oriented Software

The role of Test Management in the software industry has not remained the same. Few years back, testing was rarely present in the checklist of Project Manager. The strategic elements of business have guided software industry towards the realization of impact of quality in this industry. The growing complexity of today's application, combined with increased pressure and skyrocketing costs of app...

Evaluation of fractal dimensions for analysis of aggregate stability

Software Tools for the Analysis of Functional Magnetic Resonance Imaging

Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging of brain functions. Currently, there is a large variety of software packages for the analysis of fMRI data, each providing many features for users. Since there is no single package that can provide all the necessary analyses for the fMRI data, it is helpful to know the features of each software package. ...