Correlation operation as measure of similarity between two functions

In summary, the correlation operation is a measure of similarity between two functions. The cross-correlation function is used to deal with cases where the functions are not the same.
  • #1
HJ Farnsworth
128
1
Hello,

I recently started going through some lecture notes on linear systems and Fourier optics. (By the way, I just started with these, but so far the lecture notes are excellent. If anyone is looking to learn the subject but doesn't want to spend money on a textbook, the lecture notes, and the videos of the lectures, can be found at https://itunes.apple.com/us/itunes-u/opti512r-linear-systems-fourier/id413140966?mt=10).

In lecture 5, it says,

A very important operation that appears in many aspects of physics is the correlation operation. Correlation is a measure of similarity between two functions, and is defined (for complex functions) as

[itex]\gamma _{fg}(x)=\int _{-\infty}^{\infty} f(\alpha)g^{*}(\alpha -x)d\alpha.[/itex]

Does anyone know, in what sense is this integral a measure of the similarity between [itex]f[/itex] and [itex]g[/itex]?

I'm looking for both conceptual answers, i.e., an intuitive sense of why this integral corresponds to similarity, and quantitative answers, i.e., if the integral evaluates to this, [itex]f[/itex] and [itex]g[/itex] are "similar", but if it evaluates to that, then [itex]f[/itex] and [itex]g[/itex] are "dissimilar".

Thanks very much.

-HJ Farnsworth
 
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  • #2
Let us first consider ##x=0## and let us make all functions positive valued, then we are dealing with the quantity

[tex]\int_{-\infty}^{+\infty} f(\alpha)g(\alpha)d\alpha[/tex]

This formula can be used to see how much the functions ##g## and ##f## match. Indeed, this integral will be maximized if ##f=g##. In a formal sense, we have

[tex]\int_{-\infty}^{+\infty} f(\alpha)g(\alpha)d\alpha\leq \int_{-\infty}^{+\infty} \max\{f(\alpha),g(\alpha)\} d\alpha[/tex]

and equality holds if ##f=g##. So if we get a "high" value for this, then the functions match a lot, otherwise, they don't match a lot.

But now let's look at ##\sin## and ##\cos##. They certainly are not the same functions, but they are similar. In fact, they are the same function up to a phase factor. Indeed,

[tex]\sin(\alpha) = \cos(\alpha - \pi/2)[/tex]

The cross-correlation function is used to deal with this case. So by definition, we have

[tex](f\ast g)(x) = \int_{-\infty}^{+\infty} f(\alpha)g(\alpha - x)d\alpha[/tex]

So for any phase constant, what this does is translate ##g## by that phase constant and then measure the similarity of the functions.

As such, we get several values, one for each ##x##. So we get the function ##(f\ast g)##. High values of this function indicate that ##f## and the translated ##g## match a lot, low values that they don't match a lot. So the cross-correlation can be used to indicate by how much we should translate a function for them to match.
 
  • #3
Thanks for the reply, micromass!

I understand - like functions will be in the same quadrant of the complex plane (or even better, "point" in the same direction in the complex plane) at the same time, or restricting ourselves to real functions, like functions will be positive or negative at the same time. This results in (ignoring the [itex]-x[/itex] for the moment) higher values of the given integral, since [itex]f\times g^{*}[/itex] will be positive if [itex]\mathrm{Re}(f)/\mathrm{Im}(f) = \mathrm{Re}(g)/\mathrm{Im}(g)[/itex]. Unlike functions won't be expected to point in the same direction in the complex plane at the same time, resulting in lower values for the given integral. Throwing the [itex]-x[/itex] back in there, we have a variable that can be adjusted to increase or decrease the similarity.

So, similarity in this sense means "degree to which the two functions point in the same direction in the complex plane".

Thanks again.

-HJ Farnsworth
 
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FAQ: Correlation operation as measure of similarity between two functions

What is correlation operation and how is it used to measure similarity between two functions?

Correlation operation is a mathematical tool used to measure the similarity between two functions. It involves comparing how the two functions change in relation to each other. This can be done by calculating the correlation coefficient, which provides a value between -1 and 1 that indicates the strength and direction of the relationship between the two functions.

How is correlation operation different from other measures of similarity, such as cosine similarity?

Correlation operation takes into account both the magnitude and direction of the relationship between two functions, while cosine similarity only considers the angle between the two vectors. Additionally, correlation operation can be used to measure both linear and non-linear relationships, while cosine similarity is limited to measuring linear relationships.

Can correlation operation be used to compare functions with different scales or units?

Yes, correlation operation can be used to compare functions with different scales or units. This is because the correlation coefficient is a unitless measure and is not affected by the scale or units of the functions being compared. However, it is important to note that the interpretation of the correlation coefficient may differ when comparing functions with different scales or units.

What are some limitations of using correlation operation as a measure of similarity between two functions?

One limitation of correlation operation is that it only measures the linear relationship between two functions. Therefore, it may not accurately capture non-linear relationships. Additionally, correlation operation does not take into account the magnitude of the differences between the two functions, which may be important in certain scenarios.

How can correlation operation be used in scientific research?

Correlation operation is commonly used in scientific research to analyze and identify relationships between variables. It can be used to study the correlation between various factors and outcomes, and can also be used to compare different data sets or models. However, it should always be interpreted in conjunction with other statistical measures and should not be used as the sole indicator of similarity between two functions.

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