---
# System prepended metadata

title: Proving Randomly Sampling from a Points Evenly Distributed Sphere not Work

---

# Proving Randomly Sampling from a Points Evenly Distributed Sphere not Work

$$
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$$
$$
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$$
$$
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$$

## Reference
[# Foundations of Data Science: Chapter 2.5, Theorem 2.9](https://www.cs.cornell.edu/jeh/book.pdf)

## Pointset Generator and Theorem
### Pointset Generator
From the book [# Foundations of Data Science](https://www.cs.cornell.edu/jeh/book.pdf) Chapter 2.5, they described a uniformly random points generating method by using spherical Gaussian.
![](https://i.imgur.com/QyyNxQV.png)

We can use this method to generate our pointset $P$ on a sphere.

### Theorem 2.9: Gaussian Annulus Theorem
![](https://i.imgur.com/pEuDEZA.png)
In our cases, we set $x'$ is sampling from the spherical gaussian distribution of point $p_i \in \R^d$, this theorem is about
$$
Pr(\norm{x' - p_i} > \sqrt{d} + \beta \cup \norm{x' - p_i} < \sqrt{d} - \beta) \le 3e^{-c\beta^2}
$$

This also implies that
$$
Pr(\norm{x' - p_i} < \sqrt{d} - \beta) \le 3e^{-c\beta^2}
$$

## Prove it is not a constant approximation for sampling polynomial points

### Goal
We want to prove it is not a constant approximation for sampling polynomial points from the Gaussian KDE PDF of an evenly points distributed sphere. And for a sampling point $x'$, we have the following:
*With high probability, there is **no such** point from $poly(n)$ sampling points satisfies*
$$
c\sumpp e^{-\norm{x' - p}^2}\ge \sumpp e^{-\norm{x^* - p}^2}, s.t.\ c\ge 1 \ is\ a\ constant.
$$

Also, we required $\norm{x^* - p}^2 \le \log n/10$, otherwise, $\sumpp e^{-\norm{x^* - p}^2} < o(n)$, the $x^*$ should be located at a point instead of the center of the sphere.

### Definations
#### Pointset and Sphere
Assume all points of $P = \{p_1, p_2,..., p_n\}\subset \R^{n\times d}$ are evenly distributed on a sphere with radius $r = \sqrt{\log n}$. We have $\norm{x^* - p_i}^2 = \log n$.

#### Points and Dimensions
Let $d = O(n)$ and $\beta = \sqrt{n}/2$, then we can have $\sqrt{d} - \beta > r$ when $n$ is large.

#### Mass Concentrated Annulus $A_i$ and Empty Centered Ball $\B_i$
We have each point $p_i$ is a Spherical Gaussian distribution, and $3e^{-c\beta^2}$ of the total mass is concentrated on the annulus $A_i$ between $\sqrt{d} - \beta$ and $\sqrt{d} + \beta$.

The Ball $\B_i$ which is the nearly empty ball at the center of the annulus with radius equal to $\sqrt{d} - \beta$.

### Proof
#### Part1 - only $x'$ is inside all mass empty balls there are some chance to have constant approximation

This is a fact. Only when the point $x'$ is sampling from the empty sphere which radius is $\sqrt{d} - \beta$, with some probability $x'$ is located inside the sphere.

When $x'$ is located outside the empty ball $\B_i$, we have $bn$ constant partial point which have $\norm{x' - p}^2 \ge 4\norm{x^* - p}^2$. Then, we have
$$
\sumpp e^{-\norm{x' - p}^2}\le b\sumpp e^{-4\norm{x^* - p}^2} = e^{-3\norm{x^* - p}^2}\cdot b\sumpp e^{-\norm{x^* - p}^2} = b(n)^{-3}\cdot \sumpp e^{-\norm{x^* - p}^2}
$$


#### Part 2 - sampling point $x'$ is inside the empty mass ball $\B_i$ with high probability:

For pointset $P$ can be uniformly random sampling on the surface of the sphere, we can assume that all points inside $P$ are independent.

Based on the Gaussian Annulus Theorem, we have
$$
Pr(x'\in \B_1) \le 3e^{-cn}\\
Pr(x'\in \B_2) \le 3e^{-cn}\\
...\\
Pr(x'\in \B_n ) \le 3e^{-cn}\\
$$

Then, we calculate the probability of $x'$ is inside any $\B_i$, by union bound we do not require $\B_i$ is independent anymore.
$$
\begin{align}
Pr(x' \in \B_1 \cup x' \in \B_2 \cup ...) &\le \sum_i^n Pr(x'\in \B_i)\\
&= 3ne^{-cn}\\
&= 3e^{-cn + \log n}
\end{align}
$$

#### Part 3 - sampling $poly(n)$ points is not enough for a constant approximation with high probability

We sample $q = n^{c'}$ points, and the probability of there is at least one point instead $\B_i$ is:
$$
\begin{align}
Pr(\bigcup_i^q x'_i \in \B_1 \cup x'_i \in \B_2 \cup ...) &\le \bigcup_i^q Pr(x'_i \in \B_1 \cup x'_i \in \B_2 \cup ...)\\
&\le 3q\cdot e^{-cn} \\
&= \frac{n^{c'}}{3e^{cn}}\\
&= \frac{e^{c'\log n}}{3e^{cn}}\\
&= \frac{1}{3e^{cn - c'\log n}}
\end{align}
$$

With selecting $poly(n)$ points, the probability of there is at least one point inside all $\B_i$ is less than $1/3e^{cn - c'\log n}$.
