Abstract
In this paper, we propose a general framework for high-dimensional data reduction using unsupervised Bayesian model. The framework assumes that the pixel reflectance results from linear combinations of pure component spectra contaminated by an additive noise. The constraints are naturally expressed in unsupervised Bayesian literature by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. Experimental results on hyperspectral data demonstrate useful properties of the proposed reduction algorithm.
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Jin, L., Wan, W., Wu, Y., Cui, B., Yu, X. (2010). A General Framework for High-Dimensional Data Reduction Using Unsupervised Bayesian Model. In: Li, K., Li, X., Ma, S., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Communications in Computer and Information Science, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15859-9_14
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DOI: https://doi.org/10.1007/978-3-642-15859-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15858-2
Online ISBN: 978-3-642-15859-9
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