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Representing a signal in a particular basis involves finding the unique set of expansion coefficients in that basis. While there are many advantages to signal representation in a basis, particularly an orthogonal basis, there are also disadvantages.

The ability of a basis to provide a sparse representation depends on how well the signal characteristics match the characteristics of the basis vectors. For example, smooth continuous signals are sparsely represented in a Fourier basis, while impulses are not. A smooth signal with isolated discontinuities is sparsely represented in a wavelet basis. However, a wavelet basis is not efficient at representing a signal whose Fourier transform has narrow high frequency support.

Real-world signals often contain features that prohibit sparse
representation in any single basis. For these signals, you want the
ability to choose vectors from a set not limited to a single basis.
Because you want to ensure that you can represent every vector in
the space, the *dictionary* of vectors you choose
from must span the space. However, because the set is not limited
to a single basis, the dictionary is not linearly independent.

Because the vectors in the dictionary are not a linearly independent set, the signal representation in the dictionary is not unique. However, by creating a redundant dictionary, you can expand your signal in a set of vectors that adapt to the time-frequency or time-scale characteristics of your signal. You are free to create a dictionary consisting of the union of several bases. For example, you can form a basis for the space of square-integrable functions consisting of a wavelet packet basis and a local cosine basis. A wavelet packet basis is well adapted to signals with different behavior in different frequency intervals. A local cosine basis is well adapted to signals with different behavior in different time intervals. The ability to choose vectors from each of these bases greatly increases your ability to sparsely represent signals with varying characteristics.

Define a *dictionary* as a collection of
unit-norm elementary building blocks for your signal space. These
unit-norm vectors are called *atoms*. If the atoms
of the dictionary span the entire signal space, the dictionary is *complete*.

If the dictionary atoms form a linearly-dependent set, the dictionary
is *redundant*. In most applications of matching
pursuit, the dictionary is complete and redundant.

Let {φ_{k}} denote the atoms of a
dictionary. Assume the dictionary is complete and redundant. There
is no unique way to represent a signal from the space as a linear
combination of the atoms.

$$x={\displaystyle \sum _{k}{\alpha}_{k}}{\varphi}_{k}$$

An important question is whether there exists a *best* way.
An intuitively satisfying way to choose the *best* representation
is to select the φ_{k} yielding the largest
inner products (in absolute value) with the signal. For example, the
best single φ_{k} is

$$\underset{k}{\text{max}}|\text{\hspace{0.05em}}<x,\text{\hspace{0.05em}}\text{\hspace{0.05em}}{\varphi}_{k}>\text{\hspace{0.05em}}|$$

The central problem in *matching pursuit* is
how you choose the optimal *M*-term expansion of
your signal in a dictionary.

Let Φ denote the dictionary of atoms as a N-by-M matrix with M>N. If the complete, redundant dictionary forms a frame for the signal space, you can obtain the minimum L2 norm expansion coefficient vector by using the frame operator.

$${\Phi}^{\u2020}={\Phi}^{*}{(\Phi \text{\hspace{0.05em}}\text{\hspace{0.05em}}{\Phi}^{*})}^{-1}$$

However, the coefficient vector returned by the frame operator does not preserve sparsity. If the signal is sparse in the dictionary, the expansion coefficients obtained with the canonical frame operator generally do not reflect that sparsity. Sparsity of your signal in the dictionary is a trait that you typically want to preserve. Matching pursuit addresses sparsity preservation directly.

Matching pursuit is a greedy algorithm that computes the best
nonlinear approximation to a signal in a complete, redundant dictionary.
Matching pursuit builds a sequence of sparse approximations to the
signal stepwise. Let Φ= {φ_{k}} denote
a dictionary of unit-norm atoms. Let *f* be your
signal.

Start by defining

*R*=^{0}f*f*Begin the matching pursuit by selecting the atom from the dictionary that maximizes the absolute value of the inner product with

*R*=^{0}f*f*. Denote that atom by φ_{p}.Form the residual

*R*by subtracting the orthogonal projection of^{1}f*R*onto the space spanned by φ^{0}f_{p}.$${R}^{1}f={R}^{0}f-\langle {R}^{0}f,{\varphi}_{p}\rangle {\varphi}_{p}$$

Iterate by repeating steps 2 and 3 on the residual.

$${R}^{m+1}f={R}^{m}f-\langle {R}^{m}f,{\varphi}_{k}\rangle {\varphi}_{k}$$

Stop the algorithm when you reach some specified stopping criterion.

In *nonorthogonal* (or basic) matching pursuit,
the dictionary atoms are not mutually orthogonal vectors. Therefore,
subtracting subsequent residuals from the previous one can introduce
components that are not orthogonal to the span of previously included
atoms.

To illustrate this, consider the following example. The example is not intended to present a working matching pursuit algorithm.

Consider the following dictionary for Euclidean 2-space. This dictionary is an equal-norm frame.

$$\{\left(\begin{array}{c}1\\ 0\end{array}\right),\left(\begin{array}{c}1/2\\ \sqrt{3}/2\end{array}\right),\left(\begin{array}{c}-1/\sqrt{2}\\ -1/\sqrt{2}\end{array}\right)\}$$

Assume you have the following signal.

$$\left(\begin{array}{c}1\\ 1/2\end{array}\right)$$

The following figure illustrates this example. The dictionary atoms are in red. The signal vector is in blue.

Construct this dictionary and signal in MATLAB^{®}.

dictionary = [1 0; 1/2 sqrt(3)/2; -1/sqrt(2) -1/sqrt(2)]'; x = [1 1/2]';

Compute the inner (scalar) products between the signal and the dictionary atoms.

scalarproducts = dictionary'*x;

The largest scalar product in absolute value occurs between
the signal and `[-1/sqrt(2); -1/sqrt(2)]`

. This is
clear because the angle between the two vectors is almost π
radians.

Form the residual by subtracting the orthogonal projection of
the signal onto `[-1/sqrt(2); -1/sqrt(2)]`

from the
signal. Next, compute the inner products of the residual (new signal)
with the remaining dictionary atoms. It is not necessary to include ```
[-1/sqrt(2);
-1/sqrt(2)]
```

because the residual is orthogonal to that vector
by construction.

residual = x-scalarproducts(3)*dictionary(:,3); scalarproducts = dictionary(:,1:2)'*residual;

The largest scalar product in absolute value is obtained with `[1;0]`

.
The best two atoms in the dictionary from two iterations are ```
[-1/sqrt(2);
-1/sqrt(2)]
```

and `[1;0]`

. If you iterate
on the residual, you see that the output is no longer orthogonal to
the first atom chosen. In other words, the algorithm has introduced
a component that is not orthogonal to the span of the first atom selected.
This fact and the associated complications with convergence argues
in favor of Orthogonal Matching Pursuit (OMP).

In orthogonal matching pursuit (OMP), the residual is always
orthogonal to the span of the atoms already selected. This results
in convergence for a *d*-dimensional vector after
at most *d* steps.

Conceptually, you can do this by using Gram-Schmidt to create
an orthonormal set of atoms. With an orthonormal set of atoms, you
see that for a *d*-dimensional vector, you can find
at most *d* orthogonal directions.

The OMP algorithm is:

Denote your signal by

*f*. Initialize the residual*R*=^{0}f*f*.Select the atom that maximizes the absolute value of the inner product with

*R*=^{0}f*f*. Denote that atom by φ_{p}.Form a matrix,

*Φ*, with previously selected atoms as the columns. Define the orthogonal projection operator onto the span of the columns of*Φ*.$$P=\Phi \text{\hspace{0.05em}}\text{\hspace{0.05em}}{({\Phi}^{*}\Phi )}^{-1}{\Phi}^{*}$$

Apply the orthogonal projection operator to the residual.

Update the residual.

$${R}^{m+1}f=(I-P){R}^{m}f$$

*I*is the identity matrix.

Orthogonal matching pursuit ensures that components in the span of previously selected atoms are not introduced in subsequent steps.

It can be computationally efficient to relax the criterion that the selected atom maximizes the absolute value of the inner product to a less strict one.

$$\left|\langle x,{\varphi}_{p}\rangle \right|\ge \alpha \underset{k}{\mathrm{max}}\left|\langle x,{\varphi}_{k}\rangle \right|\text{\hspace{0.05em}},\text{\hspace{1em}}\alpha \in \left(0,1\right]$$

This is known as *weak* matching pursuit.

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