… to Euclidean distances of points placed in some (possibly high-dimensional) space. …
quadratic form qh on the higherdimensional space Rh. The resulting norm ∥x∥q := √qh(x) …

… effective method for satisfactory analysis of high dimensional … manifolds, the Euclidian
distance between the vertices … , the shortest distance known as geodesic distance is introduced …

… So, this paper presents a unique feature ordering and selection technique called Feature
Ranking and Subset Selection based on Euclidean distance (FRSSED). Two bearing …

… high dimensionality that fail to retain the effectiveness of … for high dimensionality data sets
unless the runtime is improved. Nevertheless, Euclidean distance is the most common distance …

… We applied PCA to reduce the dimension and analyzed how the effectiveness of … Euclidean
distances between the centroids of the clusters and their data points, and ρ be the distance …

… Experimental results showed the effectiveness of GLPP for … efficiency of feature extraction
in computer vision applications. SOLPP and NL-SOLPP achieved higher recognition efficiency …

… When c → 0, we recover the Euclidean distance since we … In other words, hyperbolic space
resembles Euclidean as it … the effectiveness of HyperML over other baselines in Euclidean …

… For image datasets we measured the Euclidean distance between the features produced by
… Thus, we can compute the effective value of δrel for the Poincaré ball. For the clipping value …

… to scale to highdimensional parameter spaces while retaining sample efficiency. A solution
… This kernel replaces the ARD Euclidean distance with a Mahalanobis distance, and so we …

… 7 to learn effective … effective methods for solving optimization problems – convex or not –
at scale; that is, involving possibly millions of decision variables and a possibly equally large …