Multidimensional binary search trees used for associative searching
Communications of the ACM
Self-Organizing Maps
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
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
The Architecture of Ant-Based Clustering to Improve Topographic Mapping
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Detecting Quasars in Large-Scale Astronomical Surveys
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
An ACO-based clustering algorithm
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
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
Unsupervised nearest neighbors with kernels
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
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To cope with high-dimensional data dimensionality reduction has become an increasingly important problem class. In this paper we propose an iterative particle swarm embedding algorithm (PSEA) that learns embeddings of low-dimensional representations for high-dimensional input patterns. The iterative method seeks for the best latent position with a particle swarm-inspired approach. The construction can be accelerated with k-d-trees. The quality of the embedding is evaluated with the nearest neighbor data space reconstruction error, and a co-ranking matrix based measure. Experimental studies show that PSEA achieves competitive or even better embeddings like the related methods locally linear embedding, and ISOMAP.