A Survey of Modern Questions and Challenges in Feature Extraction

The problem of extracting features from given input data is of critical importance for the successful application of machine learning. Feature extraction, as usually understood, seeks an optimal transformation from input data into a (typically real-valued) feature vector that can be used as an input for a learning algorithm. Over time, this problem has been attacked using a growing number of diverse techniques that originated in separate research communities, including feature selection, dimensionality reduction, manifold learning, distance metric learning and representation learning. The goal of this paper is to contrast and compare feature extraction techniques coming from different machine learning areas, discuss the modern challenges and open problems in feature extraction and suggest novel solutions to some of them.