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magdon · #2 09-19-2012, 05:49 PM

You can certainly implement your suggestion, but it is technically not the 3rd order polynomial transform, although it is a form of polynomial transform. You would introduce every term that is at most 3rd order to get the full 3rd order polynomial transform. Here are those terms:

$x_1,\ x_2,\ x_3$

$x_1^2,\ x_1x_2,\ x_1x_3,\ x_2^2,\ x_2x_3,\ x_2^3$

$x_1^3,\ x_1^2x_2,\ x_1^2x_3,\ x_1x_2^2,\ x_1x_3^2,\ x_1x_2x_3,\ x_2^3,\ x_2^2x_3,\ x_2x_3^2,\ x_3^3$

If you have

variables and you want the

-order polynomial transform, there are quick algorithms to generate these terms. As you can see, the number of terms rises very quickly. In this example you have done much worse than tripling. In general, for the

-order polynomial transform with

variables, the number of terms is

$\left(\begin{array}{c}d+Q\\Q\end{array}\right)=\frac{(d+Q)!}{d!Q!}.$

Quote:

Originally Posted by Daniel

I'm curious about using polynomial regression with multiple features. I understand how to use polynomial hypotheses with univariate regression, but I'm unsure how to extend this to multiple features. Let's say I want to use a cubic polynomial. Do I introduce three terms in the hypothesis for each feature x^1 and x^2 and x^3 ?? So in effect, I'm tripling the number of features (or terms in the hypothesis equation)?

Thank you.

Daniel