As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The "LFWA+FD" focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.

翻译：随着机器人应用的日益普及，海量视觉数据导致数据维度急剧增加。尽管深度学习方法能有效处理这些高维视觉数据，但由于计算资源的限制，某些特殊场景仍依赖传统机器学习方法。然而，这些高维视觉数据给传统机器学习方法带来了巨大挑战。为此，我们提出了一种面向特征选择的轻量级烟花算法与分形维数约束方法（LFWA+FD），并将其应用于机器人视觉驱动的特征选择问题。LFWA+FD通过简化烟花算法并利用分形维数约束所选特征的维度，聚焦于寻找理想特征子集，从而减少近似特征并降低原始数据中的噪声，以提升模型精度。在UCI两个公开数据集上的对比实验结果表明，所提方法能够有效选择对模型推理有用的特征子集，并去除原始数据中存在的大量噪声，从而提升模型性能。