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Results tagged with dimensionality-reduction
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user 12414
Techniques for reducing a large number of variables or dimensions spanned by data to a smaller number of dimensions while preserving as much information about the data as possible. Prominent methods include PCA, Factor Analysis, MDS, Independent Component Analysis, Multiple Correspondence Analysis, Isomap, etc. The two main subclasses of techniques: feature extraction and feature selection.
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Embedding data into a larger dimension space
are there possible benefits for embedding into a space of larger or same dimension?
In Vector Symbolic Architectures (also known as Hyperdimensional Computing) this is essential. VSAs use algebraic …
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In PCA, how to deal with features being a convenience sample from an infinite universe of po...
Principal Components Analysis (PCA) of the covariance matrix is often used to provide a reasonable approximation to the covariance matrix of some data by retaining the leading $k$ principal components …
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