FSDR：一种基于深度学习的使用离散松弛的伪时间序列数据特征选择算法
FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation
AI总结：
提出基于深度学习和离散松弛的特征选择算法FSDR，适用于高维伪时间序列数据，在超光谱数据集上优于三种常用算法，兼顾执行时间、R²和RMSE。
摘要：
应用于伪时间序列（PTS）数据的传统特征选择算法，该数据由按顺序排列但不遵循常规时间维度的观测组成，在处理高维数据时往往表现出不切实际的计算复杂度。为应对这一挑战，我们提出了一种基于深度学习（DL）的特征选择算法：通过离散松弛进行特征选择（FSDR），专为PTS数据设计。与现有特征选择算法不同，FSDR使用离散松弛（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能够容纳大量特征维度，这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，在考虑执行时间、R²和RMSE之间的平衡时，FSDR优于三种常用的特征选择算法。
英文摘要：
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 $RMSE$.
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