Intrinsic Bayesian model for high-dimensional unsupervised reduction

Abstract

This paper proposes a novel algorithm for high-dimensional unsupervised reduction from intrinsic Bayesian model. The proposed algorithm is to assume that the pixel reflectance results from nonlinear combinations of pure component spectra contaminated by additive noise. The constraints are naturally expressed in intrinsic Bayesian literature by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. The proposed algorithm consists of intrinsic Bayesian inductive cognition part and hierarchical reduction algorithm model part. The algorithm has several advantages over traditional distance based on Bayesian reduction algorithms. The proposed reduction algorithm from intrinsic Bayesian inductive cognitive model is used to decide which dimensions are advantageous and to output the recommended dimensions of the hyperspectral image. The algorithm can be interpreted as a novel fast reduction inference method for intrinsic Bayesian inductive cognitive model. We describe procedures for learning the model hyperparameters, computing the dimensions distribution, and extensions to the intrinsic Bayesian inductive cognition model. Experimental results on hyperspectral data demonstrate robust and useful properties of the proposed reduction algorithm.