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To ensure similarly oriented matches, the angle between two such vectors must be <45○.
A simple way of computing the similarity between two such vectors is to use the radial basis function (RBF) kernel, e.g. Schölkopf and Smola (2002): where σ is a kernel hyperparameter to be found by model selection.
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There are many methods of measuring the difference between two probability distribution vectors, such as Euclidean distance, Manhattan distance and Kullback Leibler divergence, etc.
In addition, the computation of hamming distance between two bit vectors has many advantages in comparison with euclidean distance, such as time and memory saving.
Commonly used local distances, such as Euclidean or Mahalanobis distances, compute the difference between two feature vectors directly [10], and they are thus of a feature-feature type.
Distance between two users or two items is the distance between two row vectors (for user kernel) or column vectors (for item kernel).
The inner product between two state vectors is a complex number known as a probability amplitude.
The MCC represents a Pearson correlation between two binary vectors.
A functional distance between experiments is defined as the distance between two pathprint vectors.
Fifty thousand simulations of the correlation between two random vectors of 14 elements were performed.
It is represented by the direction cosine between two vectors normalized by the subtraction of their own means, and its value accounts for the angle between two feature vectors instead of the vector lengths.
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