The performance of many content analysis methods heavily dependent on the features they are applied. A fundamental problem that makes the content analysis difficult is the curse of dimensionality. In this study, we propose a novel feature reduction method which adopts ensemble approach to measure the divergence between the training set and test set and use the divergence to supervise the featur...

Feature reduction is common in biosignal analysis, especially in case of quantitative EEG analysis. Mostly, summation in the spectral domain is applied to reduce the number of estimated power spectral density values, which gains between four and twelve band power values. Depending on the problem, on signals under analysis and on methods used for further processing it is an open question if such...

A common approach to improve medical image classification is to add more features to the classifiers; however, this increases the time required for preprocessing raw data and training the classifiers, and the increase in features is not always beneficial. The number of commonly used features in the literature for training of image feature classifiers is over 50. Existing algorithms for selectin...

In this project, four unsupervised feature reduction algorithms for clustering problem were investigated and experimented upon two sets of data – handwritten digits data set and the functional magnetic resonance imaging (fMRI) resting state data set. Ratio of sum of squares (RSS), leverage score (LEV), and Laplacian score (LAP) were used to rank the influences of the features in the clustering....

Feature reduction is a major preprocessing step in the analysis of highdimensional data, particularly from biomolecular high-throughput technologies. Reduction techniques are expected to preserve the relevant characteristics of the data, such as neighbourhood relations. We investigate the neighbourhood preservation properties of feature reduction empirically and theoretically. Our results indic...