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Longer titles found: Euclidean distance matrix (view)

searching for Euclidean distance 133 found (368 total)

alternate case: euclidean distance

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Gaussian distribution. Crucially, the weights depend not only on Euclidean distance of pixels, but also on the radiometric differences (e.g., range differences

eigenvectors and eigenvalues of a diffusion operator on the data. The Euclidean distance between points in the embedded space is equal to the "diffusion distance"

Morphometrics (from Greek μορϕή morphe, "shape, form", and -μετρία metria, "measurement") or morphometry refers to the quantitative analysis of form, a

when the feasible locations and demand points are in the plane with Euclidean distance as transportation cost (planar minmax Euclidean facility location

that vector similarity measures, such as dot product similarity, or euclidean distance, hold in the embedding space. These similarities are functions of

the integer pairs (a, b), where instead of the usual Euclidean distance we have a Euclidean distance defined in terms of the vector space norm ||(a,b)||=a2+ab+b2

space, analogous to the way spherical distance is inherited from Euclidean distance when the n-sphere is embedded in (n+1)-dimensional Euclidean space

networks concept, where the Mahalanobis-like distance is used instead of Euclidean distance measure. Hyper basis function networks were first introduced by Poggio

d(x,y){\displaystyle d(\mathbf {x} ,\,\mathbf {y} )} denotes the Euclidean distance between the image points represented by vectors x{\displaystyle \mathbf

real numbers, the arithmetic mean is a Fréchet mean, using the usual Euclidean distance as the distance function. The median is also a Fréchet mean, if the

for the Euclidean distance, while, provided there are at least 3 books, the first transport plan is optimal for the squared Euclidean distance. The following

others being Möbius plane and Laguerre plane). Applying the pseudo-euclidean distance d(P1,P2)=(x1′−x2′)2−(y1′−y2′)2{\displaystyle d(P_{1}

ideal constellation point and the point received by the receiver. The Euclidean distance between the two points is its magnitude. The modulation error ratio

2 * dy − dx; // discriminator // Euclidean distance of point (x,y) from line (signed) D = 0; // Euclidean distance between points (x1, y1) and (x2, y2)

‖x−xn‖2{\displaystyle \|\mathbf {x} -\mathbf {x} _{n}\|^{2}}. This squared Euclidean distance term is a particular example of a Bregman distance. Using other Bregman

associated with it, as with GPS data. T-LoCoH uses TSD rather than Euclidean distance to identify each point's nearest neighbors, resulting in hulls that

equivalence classes endowed with the obvious metric: i.e. regular Euclidean distance for two points in the same interval, and the sum of the distances

R.; Naidoo, A. (2010) "Spatial Implications Associated with Using Euclidean Distance Measurements and Geographic Centroid Imputation in Health Care Research"

the academic community. Because the RBF uses the one-dimensional Euclidean distance variable irrespective of dimensionality, the Kansa method is independent

one-point compactification of [0, ∞). Note that e extends the ordinary Euclidean distance. This cannot be done with the ordinary Euclidean metric. Let βN be

i\neq m}. Usually, in order to measure similarity the inverse of the Euclidean distance is used: ‖x−wi‖{\displaystyle \left\|{{\mathbf {x} }-{\mathbf {w}

{K}}(t)=\lambda ^{-1}\sum _{i\neq j}{\frac {I(d_{ij}<t)}{n}},} where dij is the Euclidean distance between the ith and jth points in a data set of n points, t is the

q\,\in \,K}\|p-q\|_{2}} be the diameter of K, that is, the largest Euclidean distance between any two of its points. Jung's theorem states that there exists

points in the plane, and each edge is assigned the length equal to the Euclidean distance between its endpoints. The Euclidean minimum spanning tree is the

measures. x(i,j)=|Xi−Xj|{\displaystyle x(i,j)=|X_{i}-X_{j}|}, the Euclidean distance between the ith and jth observations. t(i,j){\displaystyle t(i,j)}

points in the plane, and each edge is assigned the length equal to the Euclidean distance between its endpoints. The Euclidean minimum spanning tree is the

isochrone, isoquant and indifference curve. Consider the 2-dimensional Euclidean distance: d(x,y)=x2+y2{\displaystyle d(x,y)={\sqrt {x^{2}+y^{2}}}} A level

minimizes the total amount of horizontal motion, with ties broken by Euclidean distance. For points in the plane with distinct integer x{\displaystyle x}-coordinates

r_{i}=\left\|\left(x,y\right)-\left(sx_{i},sy_{i}\right)\right\|} denotes the Euclidean distance between collocation points (x,y){\displaystyle \left(x,y\right)} and

similarity between two face images is assessed based on the square of the Euclidean distance between the corresponding normalized vectors in the 128-dimensional

constraints intrinsic to the environment. For example, states close in Euclidean distance (such as states on opposite sides of a wall) may be far apart in manifold

group of the hyperbolic plane, preserving hyperbolic rather than Euclidean distance. (Some are depicted in drawings of Escher.) Similarly, automorphism

y is d-near K if its Euclidean distance from K is at most d; A vector y is d-deep in K if it is in K, and its Euclidean distance from any point in outside

document representations are 2n{\displaystyle 2^{n}} and the maximum Euclidean distance between pairs is n{\displaystyle {\sqrt {n}}}. As documents are added

check that both of the projection operations described minimize the Euclidean distance between input and output values. Moreover, if the algorithm succeeds

clusters of points based on Euclidean distance Conditional Euclidean clustering - clustering points based on Euclidean distance and a user-defined condition

ξ cluster extraction) using a k-d tree for index acceleration for Euclidean distance only. Python implementations of OPTICS are available in the PyClustering

D_{W}(x_{1},x_{2})^{2}=\|x_{1}'-x_{2}'\|_{2}^{2}} corresponds to the Euclidean distance between the transformed feature vectors x1′=Lx1{\displaystyle x_{1}'=Lx_{1}}

of Kruskal's algorithm on a complete graph with weights based on Euclidean distance Class Minimum spanning tree algorithm Data structure Graph Worst-case

A demo of SPFA based on Euclidean distance. Red lines are the shortest path covering (so far observed). Blue lines indicate where relaxing happens, i.e

Pythagorean theorem and its generalization, the parallelogram law. Euclidean distance is not a smooth function: the three-dimensional graph of distance

affine structure. As for vector space structure, the dot product and Euclidean distance usually are assumed to exist in Rn without special explanations. However

= {(x, y, z) : z = x2 + y2}, with the metric g being the ordinary Euclidean distance coming from the embedding of the paraboloid in Euclidean space R3

McGraw-Hill. p. 145. ISBN 9780078820625. Ingemar Ragnemalm, "The Euclidean Distance Transform", Dissertation No 304, Linköping University, 1993 Home page

A demo for Prim's algorithm based on Euclidean distance

dimensional space. The probability of a given outcome is denoted by a euclidean distance from the point that represents a lottery (probability). "EconPort

algorithm or Jarvis march Chan's algorithm Kirkpatrick–Seidel algorithm Euclidean distance transform: computes the distance between every point in a grid and

1214/aop/1039639365. Marton, K. (2004). "Measure concentration for Euclidean distance in the case of dependent random variables". Annals of Probability

transportation problem, when the "cost functional" therein is given by the Euclidean distance. List of nonlinear partial differential equations Complex Monge–Ampère

the different distances D0 to D4 used in the BIRCH algorithm as: Euclidean distance D0=‖μA−μB‖{\displaystyle D_{0}=\|\mu _{A}-\mu _{B}\|} and Manhattan

; Raichel, B. (2013), "Net and prune: A linear time algorithm for Euclidean distance problems", STOC'13: Proceedings of the 45th Annual ACM Symposium on

in 3D reconstructions of solid oxide fuel cell cathodes where the Euclidean distance sums of the centroids of a pore were divided by the length of the

sub-optimal internet routing. Most of the prior NC systems use the Euclidean distance model, i.e. embed N hosts into a d-dimensional Euclidean space Rd

the input vector mapped to the current feature map (winner), using Euclidean distance (similar to the SOM). This step can be summarized as: find q′{\displaystyle

; Raichel, B. (2013), "Net and prune: A linear time algorithm for Euclidean distance problems", STOC'13: Proceedings of the 45th Annual ACM Symposium on

input is connected with its k-nearest input vectors (according to Euclidean distance metric) and all k-nearest neighbors are connected with each other

same ray from P{\displaystyle P}, their distance is defined as the Euclidean distance. Otherwise, their distance is defined to be the sum of the Euclidean

z_{y}+d(z))}, where d(z){\displaystyle \scriptstyle d(z)} is the Euclidean distance between z and the nearest site let T be the "beach line" let Rp{\displaystyle

dynamical system is invariant under many mathematical operations. Euclidean distance is invariant under orthogonal transformations. Euclidean area is invariant

sub-optimal internet routing. Most of the prior NC systems use the Euclidean distance model, i.e. embed N hosts into a d-dimensional Euclidean space Rd

imbedding of the unit circle in the Euclidean plane. Indeed, the Euclidean distance between a pair of opposite points of the circle is only 2{\displaystyle

Schneider, Jens; Kraus, Martin; Westermann, Rüdiger (2010). "GPU-Based Euclidean Distance Transforms and Their Application to Volume Rendering". In Ranchordas

its agent given its budget, and (b) the market "almost" clears: the Euclidean distance between the sum of all allocations and the initial endowment is at

ISBN 9781461334347. Dattorro, Jon (2005-01-01). Convex Optimization & Euclidean Distance Geometry. Meboo Publishing USA. p. 96. ISBN 9780976401308. Nicola

of the tree is measured in the same way as in a graph, using the Euclidean distance between pairs of points as the weight for each edge. Thus, for instance

horocyclic segment is its Euclidean length in the model, divided by its Euclidean distance from the half-plane boundary. Therefore, in order to make the two

d_{k}=(x-x_{k})^{T}(x-x_{k})} where dk{\displaystyle d_{k}} is the squared euclidean distance between the training samples xk{\displaystyle x_{k}} and the input

It can be proved that the natural pseudodistance always equals the Euclidean distance between two critical values of the measuring functions (possibly,

d){\displaystyle (M,d)} (generally R{\displaystyle \mathbb {R} }2 with the usual euclidean distance), and a pair of contracting maps on M: d0: M→M{\displaystyle d_{0}:\

c(parent(s), neighbor) < gScore(neighbor) // c(s, neighbor) is the Euclidean distance from s to neighbor gScore(neighbor) := gScore(parent(s)) + c(parent(s)

norm ‖n→‖{\displaystyle \textstyle \|{\vec {n}}\|} is defined as the Euclidean distance from the origin to n→{\displaystyle \textstyle {\vec {n}}}, i.e.,

Gradient descent is a special case of mirror descent using the squared Euclidean distance as the given Bregman divergence. The properties of gradient descent

{(x_{m}-x_{n})^{2}+(y_{m}-y_{n})^{2}+(z_{m}-z_{n})^{2}}}} is the Euclidean distance between the mth{\displaystyle m^{\textrm {th}}} and nth{\displaystyle

stage to recover jointly the co-spread users' data using minimal Euclidean-distance measure and users' channel-gain coefficients. An enhanced CDMA version

to this database and finding candidate matching features based on Euclidean distance of their feature vectors. Lowe (2004) A robust image detector & descriptor

Specializing to the case where one of the points is the origin and the Euclidean distance between the points is r, the hyperbolic distance is:

if a distance (for example total variation distance or the usual Euclidean distance) between the probability mass functions is sufficiently small. This

A demo of Dijkstra's algorithm based on Euclidean distance. Red lines are the shortest path covering, i.e., connecting u and prev[u]. Blue lines indicate

a good approximation if E is measured at an infinitesimally small Euclidean distance from the surface of the conductor. When a surface is immersed in a

r_{i}=\left\|\left(x,y\right)-\left(x_{i},y_{i}\right)\right\|_{2}} denotes the Euclidean distance, ϕ(⋅){\displaystyle \phi \left(\cdot \right)} is the general solution

R{\displaystyle \mathbb {R} }2 → R{\displaystyle \mathbb {R} }2 such that the Euclidean distance between x and y equals the distance between f(x) and f(y) for all

replicates. The dissimilarity between dilutions was evaluated by Euclidean distance calculation based on functional assessment criteria. (3) Library construction

standard curvature of −1. When one of the points is the origin and Euclidean distance between the points is r then the hyperbolic distance is: 12ln⁡(1+r1−r)=artanh⁡r

Way), this relationship must be adjusted for redshifts and for non-Euclidean distance measures due to general relativity. For planets and other Solar System

for binary valued feature can be calculates using hamming distance, Euclidean distance and etcetera. Correlation similarity measure is recommended as the

of minimizing maxi fi where each of the functions fi measures the Euclidean distance from one of the given points. LP-type problems have also been used

An index of qualitative variation (IQV) is a measure of statistical dispersion in nominal distributions. Examples include the variation ratio or the information

If the process depends only on |x−x′|{\displaystyle |x-x'|}, the Euclidean distance (not the direction) between x{\displaystyle x} and x′{\displaystyle

factor B{\displaystyle B} can be applied to each to obtain a modified Euclidean distance. Sample observations are denoted Z(si)=zi{\displaystyle Z(\mathbf

represented as a point in this space, referred to as the "ideal point." The Euclidean distance is used as the metric to measure both the performance of a single

d_{1}} and d2{\displaystyle d_{2}} can be the standard one-dimensional Euclidean distance, yielding the following definition: for every real number ε>0{\displaystyle

protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function". Bioinformatics. 31 (21): 3499–505. doi:10.1093/bioinformatics/btv390

statistically consistent test. For most applications the exponent 1 (Euclidean distance) can be applied. The important special case of testing multivariate

such that d(L,x){\displaystyle d(\mathbf {L} ,\mathbf {x} )} is the Euclidean distance between L{\displaystyle \mathbf {L} } and x{\displaystyle \mathbf

a spanning tree of a graph with edge weights corresponding to the Euclidean distance between vertices which are points in the plane (or space). The rectilinear

the geodesic distance from p{\displaystyle p} corresponds to the Euclidean distance from the origin. In these coordinates, the metric tensor is well-approximated

generalization to the multidimensional policy space, the Weighted Euclidean Distance (WED) model ... forms the stable theoretical foundation upon which

is shown at a given significance level. The equivalence test for Euclidean distance can be found in text book of Wellek (2010). The equivalence test for

P2{\displaystyle P2} elbow to shoulder, d3 hip to knee, etc. for which the Euclidean distance d=||Pi−Pj||{\displaystyle d=||Pi-Pj||} is invariant across different

negative of the same picture are far away from each other in terms of Euclidean distance, they are at almost zero distance in terms of universal distance because

also employs a trimmed mean spatial smoothing algorithm that uses a Euclidean distance discriminator to preserve partial volume and edge information; corresponds

example). This space has as many dimensions as predictor variables. The Euclidean distance is computed from the new point to the center of each neuron, and a

of mass for each class in principal component space and reporting Euclidean distance between center of mass of two or more classes. The linear discriminant

provide an improved estimate of illumination and object color. Usually, Euclidean distance, histogram intersection, or cosine or quadratic distances are used

this well; for example, in the spherical coordinates (r, θ, φ), the Euclidean distance can be written ds2=dr2+r2dθ2+r2sin2⁡θdφ2{\displaystyle ds^{2}=dr^{2}+r^{2}\

a point in our tilted cube. The advantage is that, together with Euclidean-distance calculations of hue and chroma, this representation preserves distances

algorithm or Jarvis march Chan's algorithm Kirkpatrick–Seidel algorithm Euclidean distance transform: computes the distance between every point in a grid and

which case the trace term disappears and only the term involving the Euclidean distance between the means remains. Let μ1,μ2∈Pp(R){\displaystyle \mu _{1}

\mathbb {R} ^{d}}, and (x,y)∈E(G){\displaystyle (x,y)\in E(G)} if their Euclidean distance is at most ϵ{\displaystyle \epsilon }. The importance of this example

distance(xi(t),B(t)){\displaystyle distance(x_{i}(t),B(t))} is the euclidean distance between the fish i{\displaystyle i} position and the school barycenter

pigment colors as points on a four-dimensional sphere, such that the Euclidean distance (chord length rather than arc length) between two colors is nearly

subsequence alignment of Euclidean-flavoured DTW and z-normalized Euclidean distance similar to the popular UCR-Suite on CUDA-enabled accelerators. The

M. Coxeter (1948, 1969) writes of Kelly's proof that its use of Euclidean distance is unnecessarily powerful, "like using a sledge hammer to crack an

_{i}{\frac {C}{d^{2}}}+\omega _{i}} where di{\displaystyle d_{i}} is the Euclidean distance between the sensor node and the plume source, given by:

ρ=0.01{\displaystyle \rho =0.01} ) and d{\displaystyle d} is the Euclidean distance between the mean and the value of the pixel. We can now classify a

A{\displaystyle A} bounded. Let δ>0{\displaystyle \delta >0} be the shortest Euclidean distance between A{\displaystyle A} and B{\displaystyle B} and build a square

j) = min(C); end end distance = DP(n, m); % Function to calculate euclidean distance function [cost] = Dlp(A, B) cost = sqrt(sum((A - B) .^ 2, 2)); end

and voters choose candidates in order of proximity (measured under Euclidean distance or some other metric). Spatial models imply a different notion of

instance, and the feature vectors of the instance closest to X (by Euclidean distance) from each class. The closest same-class instance is called 'near-hit'

{L}}\end{aligned}}} where d(⋅,⋅){\displaystyle d(\cdot ,\cdot )} is the Euclidean distance, and L{\displaystyle L} is any upper bound on the inner radii of all

within the states of Austria, from a GWR and a GWNN whose the weighting metrics respectively use the Euclidean distance (ED) and travel time distance (TTD)

respective radii of the circles, and di,j{\displaystyle d_{i,j}} is the Euclidean distance between their centers. When the circles intersect, qi,j=cos⁡(θi,j){\displaystyle

number called the radius of the ball. The case p = 2 is the standard Euclidean distance function, but other values of p occur in diverse contexts such as

time spent in each functional area, and the calculating the average Euclidean distance between pairs of team members in the amount of time spent in each

one-dimensional line spanned by the stochastic gradient minimizes the Euclidean distance from the unknown(!) minimizer of f{\displaystyle f}, namely, from

neighbors are determined using a selected similarity measure (e.g., Euclidean distance, Jaccard coefficient, etc.). Artificial neural networks (ANNs): ANNs

(y-\alpha ,y+\alpha )}, and considers percolation when sites are within Euclidean distance d{\displaystyle d} of each other. Site threshold is number of overlapping

the ratio between the arc length (of the shorter of two arcs) and Euclidean distance between pairs of points, sometimes called the stretch factor. He shows

the local features is formed after filtering each sector, and the Euclidean distance of the vectors of two fingerprint images can be compared to see if

distance ⟨ℓ⟩{\displaystyle \langle \ell \rangle } relates to the Euclidean distance r{\displaystyle r}, namely ⟨ℓ⟩∼rdmin{\displaystyle \langle \ell \rangle

EUCLID defined as the average of respectively the Chi-squared and Euclidean distance between state distributions in successive sliding windows. Pairwise

dij is the distance between their points in the space. Most often, Euclidean distance function (Minkowsky distance of order 2) is used. But other distance

usually called SE(3), the group of Special (handedness-preserving) Euclidean (distance-preserving) transformations in 3 dimensions. This group has two commonly-used