Linear independence after change of basis

In summary, linear independence after change of basis refers to the property of a set of vectors to remain unique and not expressible as a linear combination of each other, even after a change of coordinate system. It is important to understand as it allows for manipulation and transformation of vectors without losing their independence. The criteria for linear independence after change of basis are the same as standard linear independence, and it can be determined by performing a change of basis using the original and new basis vectors. Practical applications include image and signal processing, as well as data compression.
  • #1
cscott
782
1
Will a set of vectors stay linearly independent after a change of basis? If it's not always true then is it likely or would you need a really contrived situation?
 
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  • #2
The linear independece or dependece of vectors does not depend on the chosen basis.
 
  • #3
indeed. look at the definition. are you using some contrived matrix version of independence? or do you have the correct basis free definition?
 

What is the concept of linear independence after change of basis?

Linear independence after change of basis refers to the property of a set of vectors to remain linearly independent even after a change of basis. This means that the vectors are still unique and cannot be expressed as a linear combination of each other, even when the coordinate system is changed.

Why is it important to understand linear independence after change of basis?

Understanding linear independence after change of basis is important because it allows us to manipulate and transform vectors and matrices without losing their independence. This is especially useful in applications such as linear transformations and change of coordinate systems.

What are the criteria for linear independence after change of basis?

The criteria for linear independence after change of basis are the same as for standard linear independence. The vectors must be unique and cannot be expressed as a linear combination of each other. In other words, the determinant of the matrix formed by the vectors must be non-zero.

How can we determine if a set of vectors is linearly independent after a change of basis?

To determine if a set of vectors is linearly independent after a change of basis, we can perform a change of basis using the matrix formed by the original and new basis vectors. If the determinant of this matrix is non-zero, then the vectors are still linearly independent. If the determinant is zero, then the vectors are linearly dependent after the change of basis.

What are some practical applications of understanding linear independence after change of basis?

Some practical applications of understanding linear independence after change of basis include image processing, signal processing, and data compression. These fields often involve transforming and manipulating data in different coordinate systems, and understanding linear independence allows for accurate and efficient calculations.

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