We present Low Distortion Local Eigenmaps (LDLE), a manifold learning technique which constructs a set of low distortion local views of a dataset in lower dimension and registers them to obtain a global embedding. The local views are constructed using the global eigenvectors of the graph Laplacian and are registered using Procrustes analysis. The choice of these eigenvectors may vary across the regions. In contrast to existing techniques, LDLE is more geometric and can embed manifolds without boundary as well as non-orientable manifolds into their intrinsic dimension.

翻译：我们展示了低扭曲本地 Eigenmaps (LDLE), 这是一种多重学习技术,它构建了一套低维度数据集的低扭曲本地观点,并注册了这些数据组,以获得全球嵌入。 本地观点是使用Laplacian 图形的全局源代码构建的,并使用Procrustes 分析注册的。 这些源代码的选择可能因区域而异。 与现有技术不同, LDLE 比较, 它的几何性更高, 可以将没有边界的元件和不适应性元件嵌入其内在维度。