Advanced feature selection methods for high dimensional data

The increasing dimensionality of data is tightly related to the phenomenon known as the curse of dimensionality. The curse of dimensionality negatively impacts data mining algorithms. To avoid negative degradation of performance data preprocessing techniques such as feature selection are applied. The primary goal of the grant proposal is to develop novel feature selection methods, utilizing concepts such as transfer learning or ensembles, for high-dimensional data with a focus on the most challenging domain: high-dimensional -small sample data. The big size of the data gave rise to a new phenomenon in data processing: deep learning. However, there are many domains where only a small number of samples are available and deep learning is not feasible. To overcome this issue transfer learning is adopted recently as a very promising solution. Therefore, we will focus on transfer learning in the context of deep learning and develop feature selection methods that would allow for more efficient deep transfer learning.