降低数据的非线性：Sloan星系光谱的局部线性嵌入
Reducing the Dimensionality of Data: Locally Linear Embedding of Sloan Galaxy Spectra
摘要：
我们介绍了本地线性嵌入（LLE）的天文社区作为一种新的分类技术，使用SDSS光谱作为一个例子数据集。LLE是一种非线性降维技术，已经在计算机感知的背景下进行了研究。我们比较LLE的性能，众所周知的光谱分类技术，如主成分分析和线比诊断。我们发现，LLE结合了这两种方法的优势，在一个单一的，连贯的技术，并导致以相对较小的计算成本改进分类的发射线光谱。我们还提出了一种数据子采样技术，保留本地的信息内容，并证明有效的创建小，高效的训练样本从一个大的，高维的数据集。提供了这种基于法律环境的分类所使用的软件。
英文摘要：
We introduce Locally Linear Embedding (LLE) to the astronomical community as a new classification technique, using SDSS spectra as an example data set. LLE is a nonlinear dimensionality reduction technique which has been studied in the context of computer perception. We compare the performance of LLE to well-known spectral classification techniques, e.g. principal component analysis and line-ratio diagnostics. We find that LLE combines the strengths of both methods in a single, coherent technique, and leads to improved classification of emission-line spectra at a relatively small computational cost. We also present a data subsampling technique that preserves local information content, and proves effective for creating small, efficient training samples from a large, high-dimensional data sets. Software used in this LLE-based classification is made available.
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