Dimensionality Reduction with Unsupervised Nearest Neighbors
Oliver Kramer
mai. de 2013 · Intelligent Systems Reference Library Livro 51 · Springer Science & Business Media
E-book
132
Páginas
reportAs notas e avaliações não são verificadas Saiba mais
Sobre este e-book
This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.
Avaliar este e-book
Diga o que você achou
Informações de leitura
Smartphones e tablets
Instale o app Google Play Livros para Android e iPad/iPhone. Ele sincroniza automaticamente com sua conta e permite ler on-line ou off-line, o que você preferir.
Laptops e computadores
Você pode ouvir audiolivros comprados no Google Play usando o navegador da Web do seu computador.
eReaders e outros dispositivos
Para ler em dispositivos de e-ink como os e-readers Kobo, é necessário fazer o download e transferir um arquivo para o aparelho. Siga as instruções detalhadas da Central de Ajuda se quiser transferir arquivos para os e-readers compatíveis.
