Date
Neighborhood Component Analysis NCA is a distance measurement learning method related to KNN. It belongs to the supervised learning method and was first proposed by Gold Berger et al. in 2004.
NCA measures sample data based on a given distance measurement algorithm to achieve classification of multivariate data. Its function is the same as the purpose of the k-nearest neighbor algorithm. It directly uses the concept of random neighbors to determine the training samples related to the test samples. Usually these training samples are labeled. This method is often used to solve model selection problems.
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.
Date
Neighborhood Component Analysis NCA is a distance measurement learning method related to KNN. It belongs to the supervised learning method and was first proposed by Gold Berger et al. in 2004.
NCA measures sample data based on a given distance measurement algorithm to achieve classification of multivariate data. Its function is the same as the purpose of the k-nearest neighbor algorithm. It directly uses the concept of random neighbors to determine the training samples related to the test samples. Usually these training samples are labeled. This method is often used to solve model selection problems.
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.