REDUCING THE DIMENSIONALITY OF DATA: LOCALLY LINEAR EMBEDDING OF SLOAN GALAXY SPECTRA
- University of Washington, Box 351580, U. W. Seattle, WA 98195-1580 (United States)
We introduce locally linear embedding (LLE) to the astronomical community as a new classification technique, using Sloan Digital Sky Survey spectra as an example data set. LLE is a nonlinear dimensionality reduction technique that 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 large, high-dimensional data sets. Software used in this LLE-based classification is made available.
- OSTI ID:
- 21301559
- Journal Information:
- Astronomical Journal (New York, N.Y. Online), Vol. 138, Issue 5; Other Information: DOI: 10.1088/0004-6256/138/5/1365; Country of input: International Atomic Energy Agency (IAEA); ISSN 1538-3881
- Country of Publication:
- United States
- Language:
- English
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