Linear Projection
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A Linear Projection is a linear transformation that maps vectors from a vector space to itself while satisfying the idempotence property (where applying it twice yields the same result as applying it once).
- Context:
- It can typically preserve Linear Subspaces by mapping vectors onto linear subspaces.
- It can typically satisfy Idempotence where P² = P for any linear projection operator P.
- It can typically decompose Vector Spaces into direct sums of its image space and kernel space.
- It can typically maintain Linear Combinations when applied to vector sums and scalar multiplications.
- It can typically leave its Image Space unchanged under repeated linear projection applications.
- ...
- It can often reduce Dimensionality by projecting high-dimensional vectors onto lower-dimensional linear subspaces.
- It can often facilitate Geometric Visualization through coordinate transformations.
- It can often enable Least Squares Approximations in statistical analysis and data fitting.
- It can often support Feature Extraction in machine learning algorithms.
- ...
- It can range from being a Simple Linear Projection to being a Complex Linear Projection, depending on its linear projection computational complexity.
- It can range from being a Low-Rank Linear Projection to being a Full-Rank Linear Projection, depending on its linear projection rank.
- It can range from being a Sparse Linear Projection to being a Dense Linear Projection, depending on its linear projection matrix density.
- ...
- It can be represented by Projection Matrices satisfying P² = P.
- It can be characterized by Eigenvalues that are either 0 or 1.
- It can be implemented through Matrix Multiplication operations.
- It can be optimized using Numerical Linear Algebra techniques.
- ...
- Examples:
- Geometric Linear Projections, such as:
- Orthogonal Projection, which minimizes distance to the linear projection subspace.
- Oblique Linear Projection, which projects along non-perpendicular directions.
- Perspective Linear Projection, used in computer graphics for 3D rendering.
- Algebraic Linear Projections, such as:
- Spectral Linear Projection, decomposing along eigenvectors.
- Canonical Linear Projection, mapping to coordinate axes.
- Component Linear Projection, extracting specific vector components.
- Application-Specific Linear Projections, such as:
- Machine Learning Linear Projections, such as:
- ...
- Geometric Linear Projections, such as:
- Counter-Examples:
- Nonlinear Transformation, which lacks the linearity property required for linear projections.
- Linear Isomorphism, which is bijective and thus not idempotent like linear projections.
- General Linear Transformation, which may not satisfy P² = P required for linear projections.
- Affine Transformation, which includes translations not present in linear projections.
- See: Matrix (Mathematics), Orthogonal Projection, Linear Transformation Operation, Vector Space, Idempotent Operator, Projection Matrix, Hilbert Space Operator, Eigenspace, Direct Sum Decomposition.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Projection_(linear_algebra)#Orthogonal_projections Retrieved:2015-2-2.
- In linear algebra and functional analysis, a projection is a linear transformation P from a vector space to itself such that . That is, whenever P is applied twice to any value, it gives the same result as if it were applied once (idempotent). It leaves its image unchanged. [1] Though abstract, this definition of "projection" formalizes and generalizes the idea of graphical projection. One can also consider the effect of a projection on a geometrical object by examining the effect of the projection on points in the object.
- ↑ Meyer, pp 386+387