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Nearest-neighbor interpolation (also known as proximal interpolation or, in some contexts, point sampling) is a simple method of multivariate interpolation in one or more dimensions. Interpolation is the problem of approximating the value of a function for a non-given point in some space when given the value of that function in points around ...
Multivariate interpolation is the interpolation of functions of more than one variable. Methods include nearest-neighbor interpolation, bilinear interpolation and bicubic interpolation in two dimensions, and trilinear interpolation in three dimensions. They can be applied to gridded or scattered data.
The diagonal lines of the "W", for example, now show the "stairway" shape characteristic of nearest-neighbor interpolation. Other scaling methods below are better at preserving smooth contours in the image.
In k-NN regression, also known as nearest neighbor smoothing, the output is the property value for the object. This value is the average of the values of k nearest neighbors. If k = 1, then the output is simply assigned to the value of that single nearest neighbor, also known as nearest neighbor interpolation.
Natural neighbor interpolation with Sibson weights. The area of the green circles are the interpolating weights, w i.The purple-shaded region is the new Voronoi cell, after inserting the point to be interpolated (black dot).
Nearest-neighbor interpolation [ edit ] One of the simpler ways of increasing image size is nearest-neighbor interpolation , replacing every pixel with the nearest pixel in the output; for upscaling, this means multiple pixels of the same color will be present.
Nearest neighbor graph in geometry; Nearest neighbor function in probability theory; Nearest neighbor decoding in coding theory; The k-nearest neighbor algorithm in machine learning, an application of generalized forms of nearest neighbor search and interpolation; The nearest neighbour algorithm for approximately solving the travelling salesman ...
Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.