Classification of Stellar Spectra with Local Linear Embedding

Abstract

We investigate the use of dimensionality reduction techniques for the classification of stellar spectra selected from the Sloan Digital Sky Survey. Using local linear embedding (LLE), a technique that preserves the local (and possibly nonlinear) structure within high-dimensional data sets, we show that the majority of stellar spectra can be represented as a one-dimensional sequence within a three-dimensional space. The position along this sequence is highly correlated with spectral temperature. Deviations from this "stellar locus" are indicative of spectra with strong emission lines (including misclassified galaxies) or broad absorption lines (e.g., carbon stars). Based on this analysis, we propose a hierarchical classification scheme using LLE that progressively identifies and classifies stellar spectra in a manner that requires no feature extraction and that can reproduce the classic MK classifications to an accuracy of one type.