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Definition
Dimensionality reduction is the process of reducing the dimension of the vector space spanned by feature vectors (pattern vectors). Various kinds of reduction can be achieved by defining a map from the original space into a dimensionality-reduced space.
Background
The feature space, i.e., the vector space spanned by feature vectors (pattern vectors) defined on d-dimensional space, can be transformed into a vector space of lower-dimension d′( < d) spanned by d′-dimensional feature vectors through linear or nonlinear transformation. This transformation allows feature vectors to be represented by lower-dimensional vectors, and various kinds of vector operations and statistical analysis, such as multivariate analysis, machine learning, clustering, and classification, become less expensive to perform. Moreover, it tackles the “curse of dimensionality,” the various...
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Maeda, E. (2014). Dimensionality Reduction. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_652
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DOI: https://doi.org/10.1007/978-0-387-31439-6_652
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