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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 ...
The count–min sketch was invented in 2003 by Graham Cormode and S. Muthu Muthukrishnan [1] and described by them in a 2005 paper. [2] Count–min sketch is an alternative to count sketch and AMS sketch and can be considered an implementation of a counting Bloom filter (Fan et al., 1998 [3]) or multistage-filter. [1]
The first moment is simply the sum of the frequencies (i.e., the total count). The second moment F 2 {\displaystyle F_{2}} is useful for computing statistical properties of the data, such as the Gini coefficient of variation.
In statistics, machine learning and algorithms, a tensor sketch is a type of dimensionality reduction that is particularly efficient when applied to vectors that have tensor structure. [ 1 ] [ 2 ] Such a sketch can be used to speed up explicit kernel methods , bilinear pooling in neural networks and is a cornerstone in many numerical linear ...
The HyperLogLog has three main operations: add to add a new element to the set, count to obtain the cardinality of the set and merge to obtain the union of two sets. Some derived operations can be computed using the inclusion–exclusion principle like the cardinality of the intersection or the cardinality of the difference between two HyperLogLogs combining the merge and count operations.
In the mathematical theory of categories, a sketch is a category D, together with a set of cones intended to be limits and a set of cocones intended to be colimits. A model of the sketch in a category C is a functor: that takes each specified cone to a limit cone in C and each specified cocone to a colimit cocone in C
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The number of hash functions, k, must be a positive integer.Putting this constraint aside, for a given m and n, the value of k that minimizes the false positive probability is