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|Affiliation||University of Bonn|
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In this note, we discuss a potential pitfall in using NumPy or SciPy methods to compute eigen decompositions of covariance matrices and show how to avoid it. In short, we ...
In this note, we discuss efficient NumPy recipes for Euclidean nearest neighbor and k-nearest neighbor searches in data sets of moderate size. Our code snippets are basically one-liners and orders ...
In this note, we show that least squares optimization is amenable to the kernel trick. This provides great flexibility in model fitting and we consider examples that illustrate this. In ...
In this note, we explore and evaluate various ways of computing squared Euclidean distance matrices (EDMs) using NumPy or SciPy. In particular, we discuss 6 increasingly abstract code snippets where ...
In this note, we study k-medoids clustering and show how to implement the algorithm using NumPy. To illustrate potential and practical use of this lesser known clustering method, we discuss ...