Skabar,A.A.(2011): Mineral prospectivity prediction from high-dimensional geoscientific data using a similarity-based density estimation model. Natural Resources Research, 20(3), 143-155.

『類似性に基づく密度見積りモデルを用いた高次元地球科学データからの鉱物の将来予想』


Abstract
 Assuming a study region in which each cell has associated with it an N-dimensional vector of values corresponding to N predictor variables, one means of predicting the potential of some cell to host mineralization is to estimate, on the basis of historical data, a probability density function that describes the distribution of vectors for cells known to contain deposits. This density estimate can then be employed to predict the mineralization likelihood of other cells in the study region. however, owing to the curse of dimensionality, estimating densities in high-dimensional input spaces is exceedingly difficult, and conventional statistical approaches often break down. This article describes an alternative approach to estimating densities. Inspired by recent work in the area of similarity-based learning, in which input takes the form of a matrix of pairwise similarities between training points, we show how the density of a set of mineralized training examples can be estimated from a graphical representation of those examples using the notion of eigenvector graph centrality. We also show how the likelihood for a test example can be estimated from these data without having to construct a new graph. Application of the technique to the prediction of gold deposits based on 16 predictor variables shows that its predictive performance far exceeds that of conventional density estimation methods, and is slightly better than the performance of a discriminative approach based on multilayer perceptron neutral networks.

Key Words: Mineral deposit prediction; density estimation; eigenvector graph centrality; similarity-based learning』

Introduction
Similarity-based density estimation
 Eigenvector graph centrality
 Estimating likelihoods on test data
 Converting distances to similarities
Empirical results
 A two-dimensional illustrative example
  Gaussian mixture models
  Similarity-based and kernel approaches
 Full 16-dimensional input space
Discussion and concluding remarkes
References


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