# Proving Linear Regression

Last time, we introduced linear regression as a new class of learners which we called linear. Let’s start with a little recap…

We considered a set of features $\mathfrak{X}$ together with labels which in turn took values in a finite-dimensional inner product space $\mathfrak{y}$. We next considered any finite-dimensional subspace $\mathfrak{H}\subset \mathfrak{y}^\mathfrak{X}$ of the vector space of functions $\mathfrak{X}\longrightarrow \mathfrak{y}$ as the possible hypotheses as well as a dataspace $\mathfrak{D}$ consisting of finite subsets of $\mathfrak{X}\times \mathfrak{y}$ which separate the hypothesis space $\mathfrak{H}$. We finally associated to any dataset $\Delta \in \mathfrak{D}$ and any hypothesis $f \in \mathfrak{H}$ a cost which was simply the norm of the vector $\big(f(x)-y)\big)_{(x,y) \in \Delta}$ in the space $\mathfrak{y}^\Delta$ or more explicitely: $$c(\Delta, f) = \vert \vert (f(x)-y)_{(x,y)\in \Delta}\vert \vert_{\mathfrak{y}^\Delta} = \sum_{(x,y) \in \Delta}\sqrt{\vert \vert f(x)-y\vert \vert^2_\mathfrak{y}}$$ We then claimed (without proof )that this indeed defines a sharp learner in the sense that for any dataset $\Delta \in \mathfrak{D}$, the learned hypothesis which by definition is given by $$h_\Delta := \textrm{argmin}_{f\ \in \mathfrak{H}} c(\Delta,f)$$ was indeed well defined

The proof is actually rather easy, and today we will guide you through the steps:

Let’s begin by recalling the following fact from linear algebra:

Let $W\subset V$ be a subspace of a finite dimensional inner product space $V$. Then the following are equivalent:
1. the vector $v-w$ is orthogonal to the subspace $W$
2. $\vert \vert v-w\vert \vert \le \vert \vert v-u\vert \vert$ $, \forall u \in W$
Moreover, the vector $w$ satisfying either condition is necessarily unique

The above lemma tells us that the projection onto the subspace $W$ defined as $\pi(v)=\text{argmin}_{w \in W}\vert \vert v-w\vert \vert$ coincides with the other notion of projection of onto the subspace $W$ by considering the decomposition of $V$ into $V=W\oplus W^{\perp}$ and writing $v = w+(v-w)$. We will call $w$ map the projection of $v$ onto $W$ as no confusion can arise.

In fact we’ll be interested in a slight variation of the above definition: instead of a subspace, we’ll consider a linear map $f:V\longrightarrow W$ and assume that $W$ is now a finite dimensional inner product space. We let $U$ denote the subspace $U=\text{im}(f)\subset W$. Now, for any vector $w \in W$ we can ask what the projection of $w$ onto $U$ looks like. There is a very nice answer to this question:

The following are equivalent:
1. $f(v)$ is the projection of $w$ onto $U$
2. the vector $v \in V$ satisfies $(f^* \circ f)(v) = f^*(w)$
The above equation is called the normal equation.
An important remark is that if the map $f$ is injective, this vector $v$ is necessarily unique! (since we know that $f(v)$ is unique by the previous lemma, and $f(v)$ can have only one pre-image by injectivity in this case). In this case we'll use the following notation to denote $v$: $$v= f^+(w)$$
It turns out that this above lemma is exactly what we need to prove the promised claim of linear regression being indeed a sharp learner.

Here's how to do it: Assume the dataset $\Delta \in \mathfrak{D}$ is given.. Then $\Delta$ consists of a finite set of couples $(x,y)\in \mathfrak{X}\times \mathfrak{y}$ and separates $\mathfrak{H}$, in the sense that if $f$ and $g$ coincide on $\Delta$, then $f=g$. Now consider the map:
$$\textrm{ev}_\Delta: \mathfrak{H}\longrightarrow \mathfrak{y}^\Delta: f \longrightarrow (f(x))_{x\in \Delta}$$
Note that $\mathfrak{y}^\Delta$ being a finite product of finite-dimensional inner product spaces is itself an inner product space! Now, $\text{ev}_\Delta$ is clearly a linear map, so we let $U=\text{im}(\text{ev}_\Delta)$ denote the image subspace in $\mathfrak{y}^\Delta$. Now the separation condition of $\Delta$ translates exactly into the fact that $\text{ev}_\Delta$ is injective. We can thus apply the previous lemma to write: $$h_\Delta = \text{ev}^+_\Delta\big((y)_{(x,y) \in \Delta}\big)$$ In other words, $h_\Delta$ is the unique hypothesis in $\mathfrak{H}$ such that $\textrm{ev}_\Delta (h_\Delta)$ is the projection of the labels $\big((y)_{(x,y)\in \Delta}\big)$ of the dataset onto the image of $\text{ev}_\Delta$ (it's a mouthful, I agree). The first lemma now implies that this in turn is equivalent to $$h_\Delta =\text{argmin}_{f\in U}\vert \vert \text{ev}_\Delta(f)-(y)_{(x,y) \in \Delta} \vert \vert_{\mathfrak{y}^\Delta}$$ But that in turn is equivalent to $$= \text{argmin}_{f\in U}\vert \vert \big(f(x)_{(x,y)\in \Delta}\big)-\big((y)_{(x,y) \in \Delta}\big) \vert \vert_{\mathfrak{y}^\Delta}=\vert \vert (f(x)-y)_{(x,y)\in \Delta}\vert \vert_{\mathfrak{y}^\Delta}$$ Which is exactly what we wanted! We can summarize:
The setup $\bigg(\mathfrak{X},\mathfrak{y},\mathfrak{H},\mathfrak{D},c\bigg)$ where $\mathfrak{X}$ is any set, $\mathfrak{y}$ a finite-dimensional inner product space $\mathfrak{H}\subset \mathfrak{y}^\mathfrak{X}$ a finite-dimensional subspace, $\mathfrak{D}$ consists of finite subsets of $\mathfrak{X}\times \mathfrak{y}$ that separate $\mathfrak{H}$ and $c(\Delta, f) = \vert \vert \big(f(x)-y\big)_{(x,y\in \Delta}\vert \vert_{\mathfrak{y}^\Delta}$ defines a sharp learner. The learned hypothesis is given as $$h_\Delta = \text{ev}_\Delta^+\big((y)_{(x,y) \in \Delta}\big)$$ where $$\text{ev}_\Delta: \mathfrak{H}\longrightarrow \mathfrak{y}^\Delta: f \mapsto (f(x))_{x\in \Delta}$$