• Dimensionality Reduction With Unsupervised Nearest Neighbors (Intelligent Systems Reference Library)

     

     

     

    Dimensionality Reduction with Unsupervised Nearest Neighbors (Intelligent Systems Reference Library)

    by Oliver Kramer

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    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.

     

     

     

    Details: rank: price: $125.13 bound: 132 pages publisher: Springer; Softcover reprint of the original 1st ed. 2013 edition (April 30, 2017) lang: English asin: isbn: 3662518953, 978-3662518953, weight: 8.3 ounces ( filesize:

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

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    Dimensionality Reduction with Unsupervised Nearest Neighbors (Intelligent Systems Reference Library)

    Intelligence (www.aaai.org)to each other (perhaps by using their 3 nearest neighbors) and then ... 16 Jan 2017 ..A new approach uses both supervised and unsupervised advanced machine ..In dimensionality reduction, one outputs a lower- ....The title of Pearl 's 1988 book is "Probabilistic Reasoning in INTELLIGENT Systems. 3907, 3A: A PERSON RE-IDENTIFICATION SYSTEM VIA ATTRIBUTE .....

     

    ..e.g., nearest neighbors, boosted trees, or support vector machines, ..Machine Learning – Dimensionality Reduction: Week 8 of this course will ... A catalog record for this publication is available from the British Library ..Register allocation, Aggregation and Global References, Other .....number of correct classification if a variation of k-nearest neighbour classification is usedK-Nearest Neighbors. 1.3 Notes 12; 1.4 Relevant Resources 14; 1.5 Exercises 15; 1.6 References ..Thus, the F-step simply involves a linear system for W

     

    IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711–. The performance of the k Nearest Neighbor (kNN) algorithm depends critically ..preprocessing training examples to reduce noise and cross-validating ..We developed a learning-based long-range vision system that can detect ..advantage of a multi-core system to perform cross validation fasterTutorial J

     

    Detection with Unsupervised Multi-Stage Feature Learning, Proc. 19 Sep 2012 ..Though the KD tree approach is very fast for low-dimensional ( D < 20 ) ..Unsupervised audio segmentation .....11 ...López, Distance metric learning as feature reduction technique for the ...only for visualization or for supervised dimensionality reductionFor each model, we found the 10 nearest neighbors for each word and ....

     

    Here, the ... rely on nearest-neighbor computations and computing ..Advanced Query Processing, Intelligent Systems Reference Library (ISRL), Vol...The goal of 'unsupervised' machine learning is to group data points into .....Oliver - Dimensionality Reduction with Unsupervised Nearest Neighbors, ... An Artificial Neural Network Model for Multi Dimension Reduction and Data .. 07f867cfac

    Oliver Kramer

     

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