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| When two vectors do not exist in the same column space, the best approximation of one in the other's columns space is called a '''projection'''. | When a vector does not exist in a column space, the '''projection''' is the best approximation of it in linear combinations of that column space. |
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| Given two vectors ''a'' and ''b'', we can '''project''' ''b'' onto ''a'' to get the best possible estimate of the former as a multiple of the latter. This projection ''p'' has an error term ''e''. | Given vectors ''a'' and ''b'', ''a'' can be projected into ''C(b)'', the column space of ''b''. This projection ''p'' has an error term ''e''. |
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| The factor which converts ''a'' into an estimate is notated as ''x̂'', so that ''p = ax̂''. The error term can be characterized by ''e = b - p'' or ''e = b - ax̂''. | |
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| ''a'' is [[LinearAlgebra/Orthogonality|orthogonal]] to ''e''. Therefore, ''a^T^(b - ax̂) = 0''. This simplifies to ''x̂ = (a^T^b)/(a^T^a)''. Altogether, the projection is characterized as ''p = a(a^T^b)/(a^T^a)''. | |
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| A matrix '''''P''''' can be defined such that ''p = '''P'''b''. The '''projection matrix''' is ''(aa^T^)/(a^T^a)''. The column space of '''''P''''' (a.k.a. ''C('''P''')'') is the line through ''a'', and its rank is 1. | === Trigonometric Approach === |
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| Incidentally, '''''P''''' is [[LinearAlgebra/MatrixProperties#Symmetry|symmetric]] (i.e. '''''P'''^T^ = '''P''''') and [[LinearAlgebra/MatrixProperties#Idempotent|idempotent]] (i.e. '''''P'''^2^ = '''P'''''). | Projections with vectors can be calculated in terms of ''θ'' is the angle formed by ''a'' and ''b''. A vector in the direction of ''b'' with the magnitude of ''a'' is given by ''||b|| cos(θ)''. This can be called the '''scalar projection'''. However, a '''vector projection''' should have a magnitude based on how much ''a'' moved through ''C(b)''. This is captured by ''â'', the unit vector in the direction of ''a'', which can be calculated as ''a/||a||''. The projection vector is given by ''(||a|| cos(θ)) (a/||a||) = (||b|| cos(θ)) â''. === Algebraic Approach === Projections with vectors can also be calculated in terms of the vectors themselves, as they represent linear transformations. First, the [[LinearAlgebra/VectorMultiplication#Dot_Product|dot product]] can be substituted into the above formulas to give a scalar projection as ''a⋅b/||a||'' and a vector projection as ''(a⋅b/||a||) a/||a|| = (a⋅b/||a||) â''. The vector projection can then be reformulated like: ''p = (a⋅b/||a||) a/||a||'' ''p = (a⋅b/||a||^2^) a'' ''p = (a⋅b/a⋅a) a'' or: ''p = (a⋅b/||a||) â'' ''p = (â⋅b) â'' === Linear Algebraic Approach === The linear transformation from vector ''a'' to projection vector ''p'' is expressed as ''p = ax̂''. The projection carries an '''error term''' that can be characterized by ''e = b - p'' or ''e = b - ax̂''. ''a'' is [[LinearAlgebra/Orthogonality|orthogonal]] to ''e'', so ''a⋅(b - ax̂) = 0''. This simplifies to ''x̂ = (a⋅b)/(a⋅a)''. Altogether, the projection vector is ''p = a (a⋅b)/(a⋅a)''. The '''projection matrix''' '''''P''''' satisfies ''p = '''P'''b''. ''C('''P''')'', the column space of '''''P''''', is equivalent to ''C(a)''. It follows that '''''P''''' is also of [[LinearAlgebra/Rank|rank]] 1. === Properties === The projection matrix '''''P''''' is [[LinearAlgebra/MatrixProperties#Symmetry|symmetric]] (i.e. '''''P'''^T^ = '''P''''') and [[LinearAlgebra/MatrixProperties#Idempotency|idempotent]] (i.e. '''''P'''^2^ = '''P'''''). |
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| For systems of equations like '''''A'''x = b'' where there is no solution for ''x'', as in b does not exist in the column space of '''''A''''', we can instead solve '''''A'''x̂ = p'' where ''p'' estimates ''b'' with an error term ''e''. | Given a system as '''''A'''x = b'', if ''b'' is not in ''C('''A''')'', the column space of '''''A''''', then there is no possible solution for ''x''. The best approximation is expressed as '''''A'''x̂ = p'' where projection ''p'' estimates ''b'' with an error term ''e''. |
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| The error term can be characterized as ''e = b - p'' or ''e = b - '''A'''x̂'' | The error term can be characterized by ''e = b - p'' or ''e = b - '''A'''x̂''. ''e'' is orthogonal to ''R('''A''')'', the row space of '''''A'''''; equivalently it is orthogonal to ''C('''A'''^T^)''. Orthogonality in this context means that ''e'' is in the [[LinearAlgebra/NullSpaces|null space]], so '''''A'''^T^(b - '''A'''x̂) = 0''. |
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| ''e'' is [[LinearAlgebra/Orthogonality|orthogonal]] to the row space of '''''A''''' because the error term does not exist in any linear combination of the rows. The projection is more easily worked with in terms of '''''A'''^T^'', so instead think of ''e'' being orthogonal to the column space of '''''A'''^T^'', a.k.a. ''e'' is the [[LinearAlgebra/NullSpaces|null space]] of '''''A'''^T^''. Therefore, '''''A'''^T^(b - '''A'''x̂) = 0''. | The system of '''normal equations''' is '''''A'''^T^'''A'''x̂ = '''A'''^T^b''. This simplifies to ''x̂ = ('''A'''^T^'''A''')^-1^'''A'''^T^b''. Altogether, the projection is characterized by ''p = '''A'''('''A'''^T^'''A''')^-1^'''A'''^T^b''. |
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| Altogether, the system of '''normal equations''' for this problem is '''''A'''^T^'''A'''x̂ = '''A'''^T^b''. This simplifies to ''x̂ = ('''A'''^T^'''A''')^-1^'''A'''^T^b''. Altogether, the projection is characterized as ''p = '''A'''('''A'''^T^'''A''')^-1^'''A'''^T^b''. A matrix '''''P''''' can be defined such that ''p = '''P'''b''. The '''projection matrix''' is '''''A'''('''A'''^T^'''A''')^-1^'''A'''^T^''. |
The projection matrix '''''P''''' satisfies ''p = '''P'''b''. It is calculated as '''''P''' = '''A'''('''A'''^T^'''A''')^-1^'''A'''^T^''. |
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| As above, '''''P''''' is [[LinearAlgebra/MatrixProperties#Symmetry|symmetric]] (i.e. '''''P'''^T^ = '''P''''') and [[LinearAlgebra/MatrixProperties#Idempotent|idempotent]] (i.e. '''''P'''^2^ = '''P'''''). | |
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| [[Econometrics/OrdinaryLeastSquares|This should look familiar.]] A projection is inherently the minimization of the error term. | |
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| Some notes: | === Properties === |
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| 1. If '''''A''''' were a square matrix, most of the above equations would [[LinearAlgebra/MatrixInversion|cancel out]]. But we cannot make that assumption. 2. If ''b'' were in the column space of '''''A''''', then '''''P''''' would be the identity matrix. 3. If ''b'' were orthogonal to the column space of '''''A''''', then necessarily ''b'' is in the null space of '''''A'''^T^''. For that reason, projecting ''b'' onto ''e'' would give an identity matrix. In that case, '''''P'''b = 0'' and ''b = e''. |
As above, the projection matrix '''''P''''' is symmetric and idempotent. If '''''A''''' is square, the above equations simplify rapidly. If ''b'' actually ''was'' in ''C('''A''')'', then '''''P''' = '''I'''''. Conversely, if ''b'' is orthogonal to ''C('''A''')'', then '''''P'''b = 0'' and ''b = e''. === Usage === [[Statistics/OrdinaryLeastSquares|This should look familiar.]] A projection is inherently the minimization of the error term. |
Projections
When a vector does not exist in a column space, the projection is the best approximation of it in linear combinations of that column space.
Contents
Vectors
Given vectors a and b, a can be projected into C(b), the column space of b. This projection p has an error term e.
Trigonometric Approach
Projections with vectors can be calculated in terms of θ is the angle formed by a and b.
A vector in the direction of b with the magnitude of a is given by ||b|| cos(θ). This can be called the scalar projection.
However, a vector projection should have a magnitude based on how much a moved through C(b). This is captured by â, the unit vector in the direction of a, which can be calculated as a/||a||. The projection vector is given by (||a|| cos(θ)) (a/||a||) = (||b|| cos(θ)) â.
Algebraic Approach
Projections with vectors can also be calculated in terms of the vectors themselves, as they represent linear transformations.
First, the dot product can be substituted into the above formulas to give a scalar projection as a⋅b/||a|| and a vector projection as (a⋅b/||a||) a/||a|| = (a⋅b/||a||) â.
The vector projection can then be reformulated like:
p = (a⋅b/||a||) a/||a||
p = (a⋅b/||a||2) a
p = (a⋅b/a⋅a) a
or:
p = (a⋅b/||a||) â
p = (â⋅b) â
Linear Algebraic Approach
The linear transformation from vector a to projection vector p is expressed as p = ax̂. The projection carries an error term that can be characterized by e = b - p or e = b - ax̂. a is orthogonal to e, so a⋅(b - ax̂) = 0. This simplifies to x̂ = (a⋅b)/(a⋅a). Altogether, the projection vector is p = a (a⋅b)/(a⋅a).
The projection matrix P satisfies p = Pb. C(P), the column space of P, is equivalent to C(a). It follows that P is also of rank 1.
Properties
The projection matrix P is symmetric (i.e. PT = P) and idempotent (i.e. P2 = P).
Matrices
Given a system as Ax = b, if b is not in C(A), the column space of A, then there is no possible solution for x. The best approximation is expressed as Ax̂ = p where projection p estimates b with an error term e.
The error term can be characterized by e = b - p or e = b - Ax̂. e is orthogonal to R(A), the row space of A; equivalently it is orthogonal to C(AT). Orthogonality in this context means that e is in the null space, so AT(b - Ax̂) = 0.
The system of normal equations is ATAx̂ = ATb. This simplifies to x̂ = (ATA)-1ATb. Altogether, the projection is characterized by p = A(ATA)-1ATb.
The projection matrix P satisfies p = Pb. It is calculated as P = A(ATA)-1AT.
b can also be projected onto e, which geometrically means projecting into the null space of AT. Algebraically, if one projection matrix has been computed as P, then the projection matrix for going the other way is (I - P)b.
Properties
As above, the projection matrix P is symmetric and idempotent.
If A is square, the above equations simplify rapidly.
If b actually was in C(A), then P = I. Conversely, if b is orthogonal to C(A), then Pb = 0 and b = e.
Usage
This should look familiar. A projection is inherently the minimization of the error term.