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Neighbourhood components analysis
Neighbourhood components analysis is a supervised learning method for classifying multivariate data into distinct classes according to a given distance metric over the data. Functionally, it serves the same purposes as the K-nearest neighbors algorithm, and makes direct use of a related concept termed ''stochastic nearest neighbours''.
== Definition ==
Neighbourhood components analysis aims at "learning" a distance metric by finding a linear transformation of input data such that the average leave-one-out (LOO) classification performance is maximized in the transformed space. The key insight to the algorithm is that a matrix corresponding to the transformation can be found by defining a differentiable objective function for , followed by use of an iterative solver such as conjugate gradient descent. One of the benefits of this algorithm is that the number of classes can be determined as a function of , up to a scalar constant. This use of the algorithm therefore addresses the issue of model selection.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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