# Randomized low dimensional projections

One way I can get at the confusing behaviours of high dimensional distributions is to instead look at low dimensional projections of them. If I have a (possibly fixed) data matrix and a random dimensional projection, what distribution does the projection have?

This idea pertains to many others: matrix factorisations, restricted isometry properties, Riesz bases, randomised regression, compressed sensing. You could also consider these results as arising from/resulting in certain structured random matrices.

There is a confusing note soup here, sorry. You might find it better to read a coherent overview such as Meckes’ lecture slides which include a lot of important recent developments, many of which she invented:

Related: Weird slicing problems in convex geometry. For a theoretical background as to how, see .

## Random projections are kinda Gaussian

A classic introductory concept: Diaconis-Freedman effect. show that (under some mild omitted conditions), $\left\{x_{1}, \ldots, x_{n}\right\} \subseteq \mathbb{R}^{d}$ is a data set (possibly deterministic with no assumption on generating process), $$\theta$$ is a uniform random point in the sphere $$\mathbb{S}^{d-1},$$ and $\mu_{x}^{\theta}:=\frac{1}{n} \sum_{i=1}^{n} \delta_{\left\langle x_{i}, \theta\right\rangle}$ is the empirical measure of the projection of the $$x_{i}$$ onto $$\theta$$, then as $$n, d \rightarrow \infty,$$ the measures $$\mu_{x}^{\theta}$$ tend to $$\mathcal{N}\left(0, \sigma^{2}\right)$$ weakly in probability. This succinct statement is modeled on Elizabeth Meckes'.

A lesson here is that even non-Gaussian, non-independent data can become nearly i.i.d. Gaussian in low dimensional projection, as argue in their introduction.

This has been taken to incredible depth in the work of Elizabeth Meckes 1980—2020 whose papers serve as the canonical textbook in the area for now. Two foundational ones are and and there is a kind of user guide in which leverages Stein’s method a whole bunch.

## Random projections are distance preserving

What makes random embeddings go. The most famous result here is the Johnson-Lindenstrauss lemma.

A really simple proof of that is given by

• “Locality-Sensitive Hashing (LSH) is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces.” (Is this random even?)

• John Myles White explains why the Johnson-Lindenstrauss lemma is worth knowing

## Projection statistics

Another key phrase we can look for is probability on the Stiefel manifold, which is a generalization of a familiar concept from random orthonormal matrices. Stiefel manifolds generalise an orthonormal matrix because they can map between spaces of different dimension. Formally, the Stiefel manifold $$V_{k, m}$$ is the space of $$k$$ frames in the $$m$$ -dimensional real Euclidean space $$R^{m},$$ represented by the set of $$m \times k$$ matrices $$X$$ such that $$X^{\prime} X=I_{k},$$ where $$I_{k}$$ is the $$k \times k$$ identity matrix. There are some interesting cases in low dimensional projections served by $$k\ll m$$ especially $$k=1.$$

Cool results in this domain are, e.g. ; ; ; Stam (1982).

General projections results are in .

An important trick here is generating isotropic unit vectors.

Let $$Z=Z^{(q)}:=\left(Z_{1}, Z_{2}, \ldots, Z_{d}\right)$$ be a random matrix in $$\mathbb{R}^{m \times k}$$ with independent, standard Gaussian column vectors $$Z_{j} \in \mathbb{R}^{m} .$$ Then $\Theta:=Z\left(Z^{\top} Z\right)^{-1 / 2}=Z/\|Z\|_2^2$ has the desired distribution, and $\Theta=m^{-1 / 2} Z\left(I+O_{p}\left(m^{-1 / 2}\right)\right) \quad \text { as } m \rightarrow \infty.$

Vershynin’s writing on a variety of hard high-dimensional probability results is pretty accessible: ; . These bleed over into concentration results.

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