In high-dimensional spaces, mathematically driven data processing methods have recently attracted a lot of attention. We consider the situation when information is obtained by sampling a probability distribution with support on or close to a sub-manifold of Euclidean space. In this paper, we provide an innovative unsupervised learning method called Uncorrelated Neighborhood Preserving Embedding (UNPE) which identifies the underlying manifold structure of a data set and preserves the neighborhood structure of the data set. We provide a concrete formulation with UNPE, an iterative technique to demonstrate the usefulness of the framework, which has been confirmed by experimental findings on datasets Coil20, Pie, Tox, and Prostate-GE that uses three different parameters viz., F-score, NMI, and accuracy. It is observed that performance is better than the LPP algorithm by 1%, PCA by 2%, and more than 2% of LLE and LE algorithms.

中文翻译：

---

基于不相关方法的无监督特征提取

在高维空间中，数学驱动的数据处理方法最近引起了广泛的关注。我们考虑通过在欧几里得空间的子流形上或附近的支持下对概率分布进行采样来获取信息的情况。在本文中，我们提供了一种创新的无监督学习方法，称为不相关邻域保留嵌入（UNPE），它识别数据集的底层流形结构并保留数据集的邻域结构。我们与 UNPE 一起提供了一个具体的公式，这是一种迭代技术来证明该框架的有用性，这一点已通过数据集 Coil20、Pie、Tox 和 Prostate-GE 的实验结果得到证实，该数据集使用三个不同的参数，即 F 分数NMI 和准确性。观察到性能比LPP算法好1%，PCA好2%，比LLE和LE算法好2%以上。