FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo
  Time-Series Data using Discrete Relaxation
Mohammad Rahman Manzur Murshed Shyh Wei Teng Manoranjan Paul
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 $RMSE$.