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As discussed in §5.9.9, the orthogonal projection of $ y\in\mathbb{C}^N$ onto $ x\in\mathbb{C}^N$ is defined by

$\displaystyle {\bf P}_{x}(y) \isdef \frac{\left<y,x\right>}{\Vert x\Vert^2} x.

In matlab, the projection of the length-N column-vector y onto the length-N column-vector x may therefore be computed as follows:
yx = (x' * y) * (x' * x)^(-1) * x
More generally, a length-N column-vector y can be projected onto the $ M$ -dimensional subspace spanned by the columns of the N $ \times$ M matrix X:
yX = X * (X' * X)^(-1) * X' * y
Orthogonal projection, like any finite-dimensional linear operator, can be represented by a matrix. In this case, the $ N\times N$ matrix
PX = X * (X' * X)^(-1) * X'
is called the projection matrix.I.2Subspace projection is an example in which the power of matrix linear algebra notation is evident.

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``Mathematics of the Discrete Fourier Transform (DFT), with Audio Applications --- Second Edition'', by Julius O. Smith III, W3K Publishing, 2007, ISBN 978-0-9745607-4-8
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Center for Computer Research in Music and Acoustics (CCRMA),   Stanford University