期刊
SCIENCE
卷 290, 期 5500, 页码 2323-+出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.290.5500.2323
关键词
-
Many areas of science depend on exploratory data analysis and visualization. The need to analyze Large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce Locally Linear embedding (LLE), an unsupervised Learning algorithm that computes Low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for Local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve Local minima. By exploiting the local symmetries of Linear reconstructions, LLE is able to Learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据