TITLE:
Feature Selection with Non Linear PCA: A Neural Network Approach
AUTHORS:
Crescenzio Gallo, Vito Capozzi
KEYWORDS:
Feature Selection, Machine Learning, Principal Component Analysis, Artificial Neural Network
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.7 No.10,
October
31,
2019
ABSTRACT: Machine learning consists in the creation and development of algorithms that allow a machine to learn itself, gradually improving its behavior over time. This learning is more effective, the more representative is the features of the dataset used to describe the problem. An important objective is therefore the correct selection (and, possibly, reduction of the number) of the most relevant features, which is typically carried out through dimensional reduction tools such as Principal Component Analysis (PCA), which is not linear in the more general case. In this work, an approach to the calculation of the reduced space of the PCA is proposed through the definition and implementation of appropriate models of artificial neural network, which allows to obtain an accurate and at the same time flexible reduction of the dimensionality of the problem.