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：随着机器人的使用越来越普遍,大量视觉数据会导致数据维度的急剧增加。虽然深层次的学习方法能够有效地处理这些高维的视觉数据。由于计算资源的限制,一些特殊的情景仍然依赖传统的机器学习方法。然而,这些高维的视觉数据导致传统机器学习方法的巨大挑战。因此,我们提议对特征选择采用一个带有分形尺寸限制的Lite Fireworks Algorithm(LFWA+FD),并用它来解决由机器人视觉驱动的特征选择问题。“LFWA+FD”侧重于寻找理想的特征子集,简化烟花算法,通过分形维度限制选定特征的维度,这反过来又减少近似特征,并减少原始数据中的噪音,以提高模型的准确性。UCI的两个公开数据集的比较实验结果表明,拟议方法可以有效地选择有助于模型推断的子特征,并消除原始数据中存在的大量噪音,以改进性能。</s>