Multidimensional binary search trees used for associative searching
Communications of the ACM
Regularized principal manifolds
The Journal of Machine Learning Research
Principal Surfaces from Unsupervised Kernel Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
On evolutionary approaches to unsupervised nearest neighbor regression
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Dimensionality Reduction by Unsupervised K-Nearest Neighbor Regression
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 01
A particle swarm embedding algorithm for nonlinear dimensionality reduction
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Unsupervised nearest neighbors with kernels
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Unsupervised nearest neighbors with kernels
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Learning morphological maps of galaxies with unsupervised regression
Expert Systems with Applications: An International Journal
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In this paper we introduce an extension of unsupervised nearest neighbors for embedding patterns into continuous latent spaces of arbitrary dimensionality with stochastic sampling. Distances in data space are employed as standard deviation for Gaussian sampling in latent space. Neighborhoods are preserved with the nearest neighbor data space reconstruction error. Similar to the previous unsupervised nearest neighbors (UNN) variants this approach is an iterative method that constructs a latent embedding by selecting the position with the lowest error. Further, we introduce kernel functions for computing the data space reconstruction error in a feature space that allows to better handle non-linearities. Experimental studies show that kernel unsupervised nearest neighbors (KUNN) is an efficient method for embedding high-dimensional patterns.