Analysis of Galactic Spectra using Noise-Aware Learning Algorithms

We introduce a novel learning algorithm for noise elimination. Our algorithm is based on the re-measurement idea for the correction of erroneous observations and is able to discriminate between noisy and noiseless observations by using kernel methods. We apply our noiseaware algorithms to the prediction of stellar population parameters, a challenging astronomical problem. Experimental results adding noise and useful anomalies to the data show that our algorithm provides a significant reduction in error, without having to eliminate any observation from the original dataset.

• Type: Conference
• Year: 2006
• Where: FLAIRS
• Authors: H. Jair Escalante, Olac Fuentes
• Keywords: Artificial Intelligence, Challenging Astronomical Problem, FLAIRS 2006, Noise Elimination, Stellar Population Parameters