Feature normalization and likelihood-based similarity measures for image retrieval

By Selim Aksoy, Robert Martin Haralick. Pattern Recognition Letters, 2001

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A Survey of Modern Questions and Challenges in Feature Extraction (2015, methodology): “…projection, clustering, nearest neighbor, dimensionality reduction, manifold learning, distance metric learning, multiple kernel learning, representation learning, embedding, PCA, LDA, RCA, kernel PCA, Isomap, LLE, Laplacian eigenmap, neural networks, autoencoders, restricted Boltzmann machines”
A methodological approach to the classification of dermoscopy images (2007, methodology): “One of the most common normalization methods is the zscore transformation [ 41 ] given by: zij = ((xij − μj)/(3σj) + 1) 2 where xij represents the value of the jth feature of the ith sample; μj and σj are the mean and standard deviation of the jth feature, respectively.”
Standardization and Its Effects on K-Means Clustering Algorithm (2013, methodology): “Therefore, one should not employ distance measures like the Euclidean distance without having normalization of the data sets (Aksoy and Haralick, 2001; Larose, 2005).”