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The number one reason why orthogonality is essential in data science: uncorrelating features.

However, the features we are given are rarely such. There are ways to fix this, and the Gram-Schmidt process is one of them.

Here is how it works.

**Before you read on:**

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The problem is simple. We are given a set of basis vectors **a₁**,** a₂**, …, **aₙ,** and we want to turn them into an orthogonal basis **q₁**,** q₂**, …, **qₙ** such that each **qᵢ**-s represent the same information as **aᵢ**.

(A natural way to formalize our second condition is to require **q₁**,** q₂**, …, **qₖ** to span the same subspace as **a₁**,** a₂**, …, **aₖ**.)

How do we achieve such a result? One step at a time.

Let’s look at an example! Our input consists of three highly correlated but still independent three-dimensional vectors.

If we want **q₁** to represent the same information as **a₁**, the choice **q₁ =** **a₁ **will work perfectly. Nothing exciting so far.

Now, we need to pick **q₂** such that

**q₂**is orthogonal to**q₁,**and together with

**q₁**, they “represent the same information” as**a₁**and**a₂**. (That is, they generate the same subspace.)

The natural choice is to take the difference of **a₂ **and its orthogonal projection onto **q₁**.

If you are not convinced that **q₂ **is orthogonal to** q₁**, here is a quick calculation to sort it out.

If you are a visual person, here is what happens.

This projection step is the essence of Gram-Schmidt, and the entire algorithm is just its iteration.

In other words, we follow by subtracting the orthogonal projection of **a₃** onto **q₂ **and** q₁ **from** a₃** itself.

Here are the input and the output.

Overall, here is the output in the general case.

As you can see, the Gram-Schmidt process is much simpler than the intimidating formulas suggest.

From a geometric viewpoint, the Gram-Schmidt orthogonalization process constructs a set of orthogonal vectors that span the same subspace as the original vectors by iteratively subtracting the projections of previous vectors onto the current vector.

This explanation is also a part of my Mathematics of Machine Learning book, where I explain all the mathematical concepts like your teachers should have, but probably never did.

## Epsilons, no. 4: The Gram-Schmidt process

Nice post by the way, I’d comment that the axes (the angles) look a little bit strange to represent the 3d plane in some moment I thought wait why 3D vectors if the draw are in 2D space? Then I saw again and looked third dimension.

There is a mistake in the plot called final step, it’s the orthogonal projection of a3. The formulas are ok, but not the text