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Neighbourhood Components Analysis
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Geliefert zwischen Do., 05.02.2026 und Fr., 06.02.2026
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High Quality Content by WIKIPEDIA articles! Neighbourhood components analysis is an unsupervised learning method for clustering multivariate data into distinct classes according to a given distance metric over the data. Functionally, it serves the same purposes as the k-Nearest Neighbour algorithm, and makes direct use of a related concept termed stochastic nearest neighbours. Neighbourhood components analysis aims at "learning" a distance metric by finding a linear transformation of input data such that the average LOO-classification performance is maximized in the transformed space. The key insight to the algorithm is that a matrix A corresponding to the transformation can be found by defining a differentiable objective function for A, 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 k can be determined as a function of A, up to a scalar constant. This use of the algorithm therefore addresses the issue of model selection.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786130492250
- Editor Lambert M. Surhone, Miriam T. Timpledon, Susan F. Marseken
- EAN 9786130492250
- Format Fachbuch
- Titel Neighbourhood Components Analysis
- Herausgeber Betascript Publishing
- Anzahl Seiten 88
- Genre Mathematik
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