Abstract
Dimensionality reduction is the task to reduce the dimensionality of patterns, while preserving important information. Many dimensionality reduction methods focus on finding low-dimensional representations of high dimensional patterns called latent variables, latent representations, or latent embeddings. Dimensionality reduction can be employed for various tasks, e.g., visualization, preprocessing for pattern recognition methods, or for symbolic algorithms. To allow human understanding and interpretation of high-dimensional data, the reduction to 2- and 3-dimensional spaces is an important task.
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© 2013 Springer-Verlag Berlin Heidelberg
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Kramer, O. (2013). Dimensionality Reduction. In: Dimensionality Reduction with Unsupervised Nearest Neighbors. Intelligent Systems Reference Library, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38652-7_4
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DOI: https://doi.org/10.1007/978-3-642-38652-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38651-0
Online ISBN: 978-3-642-38652-7
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