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 $RMSE$.

翻译：传统的特征选择算法应用于伪时间序列（PTS）数据（即按顺序排列但不遵循传统时间维度的观测数据）时，在高维数据中常表现出不切实际的计算复杂度。为解决这一挑战，我们提出了一种基于深度学习（DL）的特征选择算法：基于离散松弛的特征选择（FSDR），专为PTS数据设计。与现有特征选择算法不同，FSDR通过离散松弛（即将离散优化问题近似为连续优化问题的过程）将重要特征作为模型参数进行学习。FSDR能够适应大量特征维度，这一能力是现有基于深度学习或传统方法所无法企及的。通过在超光谱数据集（一种PTS数据类型）上的测试，我们的实验结果表明，在执行时间、$R^2$和$RMSE$的平衡考量下，FSDR优于三种常用特征选择算法。