Characterizing Affine Independence

In summary, affine independence is a version of linear independence that does not depend on a basepoint. It is defined as having a linear combination of n points with coefficients summing to 1 that can only result in the zero vector when all coefficients are 0. This is analogous to linear independence, where the sum of coefficients must be 0. Affine independence can also be seen as varying the masses of n+1 points to form a simplex, with the set of possible centers of mass representing the affine independent points. This is equivalent to linear independence of the associated n vectors, taking one of the points as a reference point.
  • #1
Caramon
133
5
Hello, I'm currently self-studying "An Introduction to Convex Polytopes" and I'm having some trouble understanding the different characterizations of affine independence. I understand that for an n-family (x_{1},...,x_{n}) of points from R^{d}, it is affinely independent if a linear combination [tex]\lambda_{1}x_{1} +...+\lambda_{n}x_{n}[/tex] with [tex]\lambda_{1} + ... + \lambda_{n} = 0[/tex] can only have the value of the zero vector when [tex]\lambda_{1} = ... = \lambda_{n}=0[/tex]. This makes sense to me and I understand how it is an analogue to the definition of linear independence, which I would like to think I understand quite well.

It then states that, "affine independence of an n-family (x_{1},...,x_{n}) is equivalent to linear independence of one/all of the (n-1)-families:
[tex](x_{1}-x_{i},...,x_{i-1}-x_{i},x_{i+1}-x_{i},...,x_{n}-x_{i})[/tex].

I'm not immediately seeing this connection, if someone would be able to help explain it to me or send me a link so that I could understand it better, that would help. I have checked Wikipedia, MathWorld, etc. already.

Thank you very much,
-Caramon
 
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  • #2
That's not right. Your definition of affine independence is just linear independence. You want the sum of the coefficients to be 1, not 0.

Basically, affine independence is a version of linear independence that doesn't depend on basepoints. You have n+1 points. Take one of them as a reference point. That gives you n vectors. You want affine independence to mean that those vectors should be linearly independent.

It helps to think of things in terms of centers of mass. You have a n+1 points. You can give each of them a mass, and imagine that the total mass has to be 1. When you vary the masses at the various points, the set of possible centers of mass forms a simplex. This is what happens when you allow only non-negative coefficents. Allowing negative coefficients gives you the smallest plane containing the n+1 points.

Notice that if you have 3 points lying in a line, there will be more than one way (actually, a 1-parameter family of ways) to make a points in the segment containing them into a center of mass. And so on.

So, it should be plausible that affine independence of n+1 points is equivalent to linear independence of the associated n vectors, taking one of the points as a reference point.
 
  • #3
Thank you very much, that clarifies a lot.
 

What is affine independence?

Affine independence is a concept in linear algebra that refers to a set of points that are not collinear, or do not lie on the same line. In other words, they are not constrained to a single dimension, but can exist in higher dimensions.

Why is affine independence important in scientific research?

Affine independence is important in scientific research because it allows for the exploration of relationships between variables in a multidimensional space. This can provide a more comprehensive understanding of the data and can lead to more accurate and meaningful conclusions.

How is affine independence different from linear independence?

Affine independence is a generalization of linear independence. While linear independence refers to the linear combination of vectors, affine independence allows for the inclusion of a translation term, which accounts for the origin of the vectors in a higher dimensional space.

What are some methods for characterizing affine independence?

One method for characterizing affine independence is by using the rank of a matrix to determine the dimensionality of the data. Another method is through the use of convex hulls, which can identify the convex combinations of points that form the affine space.

How is affine independence used in data analysis and machine learning?

Affine independence is used in data analysis and machine learning to identify relationships between variables in a multidimensional space. It can also be used to reduce the dimensionality of data and improve the performance of machine learning algorithms.

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