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Streaming algorithm. In computer science, streaming algorithms are algorithms for processing data streams in which the input is presented as a sequence of items and can be examined in only a few passes, typically just one. These algorithms are designed to operate with limited memory, generally logarithmic in the size of the stream and/or in the ...
The median filter operates by considering a local window (also known as a kernel) around each pixel in the image. The steps for applying the median filter are as follows: Window Selection: Choose a window of a specific size (e.g., 3x3, 5x5) centered around the pixel to be filtered.
Median of medians. In computer science, the median of medians is an approximate median selection algorithm, frequently used to supply a good pivot for an exact selection algorithm, most commonly quickselect, that selects the k th smallest element of an initially unsorted array. Median of medians finds an approximate median in linear time.
Count sketch is a type of dimensionality reduction that is particularly efficient in statistics, machine learning and algorithms. [1] [2] It was invented by Moses Charikar, Kevin Chen and Martin Farach-Colton [3] in an effort to speed up the AMS Sketch by Alon, Matias and Szegedy for approximating the frequency moments of streams [4] (these calculations require counting of the number of ...
Selection algorithm. In computer science, a selection algorithm is an algorithm for finding the th smallest value in a collection of ordered values, such as numbers. The value that it finds is called the th order statistic. Selection includes as special cases the problems of finding the minimum, median, and maximum element in the collection.
Smoothing of a noisy sine (blue curve) with a moving average (red curve). In statistics, a moving average (rolling average or running average or moving mean[1] or rolling mean) is a calculation to analyze data points by creating a series of averages of different selections of the full data set. Variations include: simple, cumulative, or ...
STREAM. STREAM is an algorithm for clustering data streams described by Guha, Mishra, Motwani and O'Callaghan [3] which achieves a constant factor approximation for the k-Median problem in a single pass and using small space. Theorem — STREAM can solve the k -Median problem on a data stream in a single pass, with time O (n1+e) and space θ ...
Probabilisticdata structures. In computing, the count–min sketch (CM sketch) is a probabilistic data structure that serves as a frequency table of events in a stream of data. It uses hash functions to map events to frequencies, but unlike a hash table uses only sub-linear space, at the expense of overcounting some events due to collisions.