论文标题
全球功能的确定性解耦及其在数据分析中的应用
Deterministic Decoupling of Global Features and its Application to Data Analysis
论文作者
论文摘要
我们介绍了一种确定全局特征解耦的方法，并显示其用于改善数据分析性能的适用性，并为开放新的场所进行功能传输。我们提出了一种新的形式主义，该形式主义基于沿特征梯度遵循轨迹来定义对子曼膜的转换。通过这些转换，我们定义了一个归一化，我们证明，它允许解耦可区分的特征。通过将其应用于采样矩，我们获得了用于正库术的准分析溶液，骨质病的标准化版本不仅与平均值和方差相结合，而且还脱离了偏度。我们将此方法应用于原始数据域和过滤器库的输出中，以基于全局描述符的回归和分类问题，与使用经典（未删除）描述符相比，在性能方面获得了一致且显着的改进。
We introduce a method for deterministic decoupling of global features and show its applicability to improve data analysis performance, as well as to open new venues for feature transfer. We propose a new formalism that is based on defining transformations on submanifolds, by following trajectories along the features gradients. Through these transformations we define a normalization that, we demonstrate, allows for decoupling differentiable features. By applying this to sampling moments, we obtain a quasi-analytic solution for the orthokurtosis, a normalized version of the kurtosis that is not just decoupled from mean and variance, but also from skewness. We apply this method in the original data domain and at the output of a filter bank to regression and classification problems based on global descriptors, obtaining a consistent and significant improvement in performance as compared to using classical (non-decoupled) descriptors.
