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2012 | OriginalPaper | Chapter
A Particle Swarm Embedding Algorithm for Nonlinear Dimensionality Reduction
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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.