This is going to be a quick intuition about what it means to diagonalize a matrix that does not have full rank (i.e. has null determinant).

Every matrix can be seen as a linear map between vector spaces. Stating that a matrix is similar to a diagonal matrix equals to stating that there exists a basis of the source vector space in which the linear transformation can be seen as a simple *stretching of the space*, as re-scaling the space. In other words, diagonalizing a matrix is the same as *finding an orthogonal grid that is transformed in another orthogonal grid*. I recommend this article from AMS for good visual representations of the topic.

### Diagonalization on non full rank matrices

That’s all right – when we have a matrix from in , if it can be diagonalized, we can find a basis in which the transformation is a re-scaling of the space, fine.

But what does it mean to diagonalize a matrix that has null determinant? The associated transformations have the effect of killing at least one dimension: indeed, a x matrix of rank has the effect of lowering the output dimension by . For example, a x matrix of rank 2 will have an image of size 2, instead of 3. This happens because two basis vectors are merged in the same vector in the output, so one dimension is bound to collapse.

Let’s consider the sample matrix

which has non full rank because has two equal rows. Indeed, one can check that the two vectors go in the same basis vector. This means that instead of 3. In fact, it is common intuition that when the rank is not full, some dimensions are lost in the transformation. Even if it’s a x matrix, the output only has 2 dimensions. It’s like at the end of Inception when the 4D space in which cooper is floating gets shut.

However, is also a symmetric matrix, so from the spectral theorem we know that it can be diagonalized. And now to the vital questions: what do we expect? What meaning does it have? *Do we expect a basis of three vectors even if the map destroys one dimension?*

Pause and ponder.

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