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  2. Selection algorithm - Wikipedia

    en.wikipedia.org/wiki/Selection_algorithm

    As a baseline algorithm, selection of the th smallest value in a collection of values can be performed by the following two steps: . Sort the collection; If the output of the sorting algorithm is an array, retrieve its th element; otherwise, scan the sorted sequence to find the th element.

  3. Median of medians - Wikipedia

    en.wikipedia.org/wiki/Median_of_medians

    Thus if one can compute the median in linear time, this only adds linear time to each step, and thus the overall complexity of the algorithm remains linear. The median-of-medians algorithm computes an approximate median, namely a point that is guaranteed to be between the 30th and 70th percentiles (in the middle 4 deciles). Thus the search set ...

  4. Geometric median - Wikipedia

    en.wikipedia.org/wiki/Geometric_median

    For the 1-dimensional case, the geometric median coincides with the median.This is because the univariate median also minimizes the sum of distances from the points. (More precisely, if the points are p 1, ..., p n, in that order, the geometric median is the middle point (+) / if n is odd, but is not uniquely determined if n is even, when it can be any point in the line segment between the two ...

  5. Median - Wikipedia

    en.wikipedia.org/wiki/Median

    The median of a finite list of numbers is the "middle" number, when those numbers are listed in order from smallest to greatest. If the data set has an odd number of observations, the middle one is selected (after arranging in ascending order). For example, the following list of seven numbers, 1, 3, 3, 6, 7, 8, 9

  6. k-medians clustering - Wikipedia

    en.wikipedia.org/wiki/K-medians_clustering

    The proposed algorithm uses Lloyd-style iteration which alternates between an expectation (E) and maximization (M) step, making this an expectation–maximization algorithm. In the E step, all objects are assigned to their nearest median. In the M step, the medians are recomputed by using the median in each single dimension.

  7. Median trick - Wikipedia

    en.wikipedia.org/wiki/Median_trick

    The median trick is a generic approach that increases the chances of a probabilistic algorithm to succeed. [1] Apparently first used in 1986 [ 2 ] by Jerrum et al. [ 3 ] for approximate counting algorithms , the technique was later applied to a broad selection of classification and regression problems.

  8. Knapsack problem - Wikipedia

    en.wikipedia.org/wiki/Knapsack_problem

    algorithm FPTAS is input: ε ∈ (0,1] a list A of n items, specified by their values, , and weights output: S' the FPTAS solution P := max {} // the highest item value K := ε for i from 1 to n do ′ := ⌊ ⌋ end for return the solution, S', using the ′ values in the dynamic program outlined above

  9. Median filter - Wikipedia

    en.wikipedia.org/wiki/Median_filter

    Choose the Median Value: The median value is the middle value in the sorted list. In our case, the median value is 5. Replace the Center Pixel: Replace the original center pixel value (8) with the median value (5). Repeat for All Pixels: Repeat steps 2-5 for all pixels in the image.