By Oliver Kramer
This publication is dedicated to a singular strategy for dimensionality relief in accordance with the well-known nearest neighbor strategy that could be a strong type and regression technique. It begins with an creation to computing device studying thoughts and a real-world software from the power area. Then, unsupervised nearest friends (UNN) is brought as effective iterative approach for dimensionality aid. numerous UNN versions are constructed step-by-step, achieving from an easy iterative approach for discrete latent areas to a stochastic kernel-based set of rules for studying submanifolds with self sustaining parameterizations. Extensions that let the embedding of incomplete and noisy styles are brought. numerous optimization methods are in comparison, from evolutionary to swarm-based heuristics. Experimental comparisons to similar methodologies taking into consideration synthetic try out information units and in addition real-world info display the habit of UNN in useful eventualities. The booklet comprises various colour figures to demonstrate the brought options and to focus on the experimental results.
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Additional info for Dimensionality Reduction with Unsupervised Nearest Neighbors
In case of the ensemble ENS*, the neighborhood size of KNN is less important for both larger training sets 3−1 and 2−1 . The SVM classiﬁers compensate the negative eﬀect of too large neighborhoods, which is a good motivation for the employment of ensembles. 7 Conclusions We have shown that KNN and KNN-ensembles can serve as eﬃcient and robust recognition techniques in the practical application of load monitoring. The experimental results conﬁrmed the expectations that SVMs are a good choice in case of small training sets, while KNN shows its strengths on large training sets.
Based on gradient descent . t. to the DSRE for an iteratively growing solution. 9 Unsupervised Kernel Regression In this section, unsupervised kernel regression (UKR), one of the most prominent variants of unsupervised regression, is introduced. UKR employs the Nadaraya-Watson estimator for regression. An example for the application of UKR is the learning of low-dimensional manual actions . The task is part of human-robot communication. , the movement to open a bottle. The task is to imitate the movement with a robot hand.
Concerning the ensemble classiﬁers, we can observe low error rates in most of the experiments. The ensemble classiﬁer ENS* that employs all ﬁve classiﬁers turns out to be the most robust algorithm with low errors for all settings. It is the best or second best classiﬁer in nine of eleven cases, which is also reﬂected by the highest sum of scores. Also the KNN-ensemble classiﬁer ENSKNN achieves good results on the ﬁeld study data. The results are similar to KNN with K = 5, 7 (also the failure in case of the install data set, which cannot be compensated by KNN with K = 1).