FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation
This work addresses feature selection for researchers and practitioners dealing with high-dimensional Pseudo Time-Series data, such as in hyperspectral imaging, but it is incremental as it builds on existing deep learning and discrete relaxation techniques.
The paper tackles the challenge of feature selection for Pseudo Time-Series data, which often has high computational complexity, by introducing FSDR, a deep learning-based algorithm using discrete relaxation. Experimental results on a hyperspectral dataset show that FSDR outperforms three common algorithms in terms of execution time, R², and RMSE.
Conventional feature selection algorithms applied to Pseudo Time-Series (PTS) data, which consists of observations arranged in sequential order without adhering to a conventional temporal dimension, often exhibit impractical computational complexities with high dimensional data. To address this challenge, we introduce a Deep Learning (DL)-based feature selection algorithm: Feature Selection through Discrete Relaxation (FSDR), tailored for PTS data. Unlike the existing feature selection algorithms, FSDR learns the important features as model parameters using discrete relaxation, which refers to the process of approximating a discrete optimisation problem with a continuous one. FSDR is capable of accommodating a high number of feature dimensions, a capability beyond the reach of existing DL-based or traditional methods. Through testing on a hyperspectral dataset (i.e., a type of PTS data), our experimental results demonstrate that FSDR outperforms three commonly used feature selection algorithms, taking into account a balance among execution time, $R^2$, and $RMSE$.