# Linear Algebra - Projection

Projections: summarizing mutli-dimensional data in two or three dimensions.

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Data Mining - (Feature|Attribute) Extraction Function

Feature extraction is the second class of methods for dimension reduction. dimension reduction It creates new attributes (features) using linear combinations of the (original|existing) attributes. ...
Data Mining - Principal Component (Analysis|Regression) (PCA|PCR)

Principal Component Analysis (PCA) is a feature extraction method that use orthogonal linear projections to capture the underlying variance of the data. By far, the most famous dimension reduction approach...
Linear Algebra - Closest point in higher dimension than a plane

Solving closest point in the span of many vectors Goal: An algorithm that, given a vector b and vectors v1, . . . , vn, finds the vector in Span {v1, . . . , vn} that is closest to b. Special case:...
Linear Algebra - Orthogonalization - Building an orthogonal set of generators

Original stated goal: Find the projection of b orthogonal to the space V spanned by arbitrary vectors Input: A list of vectors over the reals output: A list of mutually orthogonal vectors such...
Spatial - Projection

See Projections: summarizing mutli-dimensional data in two or three dimensions. Data Projections Scale projection function Data domain Pixel Range DateTime (Years, Month, Date, Time) Pixel...