Feature Extraction

Abstract

Feature extraction is a procedure aimed at selecting and transforming a data set in order to increase the performance of a pattern recognition or machine learning system. Nowadays, since the amount of data available and its dimension is growing exponentially, it is a fundamental procedure to avoid overfitting and the curse of dimensionality, while, in some cases, allowing a interpretative analysis of the data. The topic itself is a thriving discipline of study, and it is difficult to address every single feature extraction algorithm. Therefore, we provide an overview of the topic, introducing widely used techniques, while at the same time presenting some domain-specific feature extraction algorithms. Finally, as a case, study, we will illustrate the vastness of the field by analysing the usage and impact of feature extraction in neuroimaging.

Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer’s disease progression

“…Traditionally, classification frameworks focus only on the brain regions that are commonly affected by the disease instead of searching for information patterns throughout the whole brain. This reduces the number of features to be processed, which partially solves the curse of dimensionality present in most statistical classifications [16]. Once the brain region is delimited, the easiest ap-proach is to use the intensity of the voxels as predictors of the different classes to be modelled, using them as inputs of the classifier.…”