Covering number

In mathematics, a covering number is the number of spherical balls of a given size needed to completely cover a given space, with possible overlaps. Two related concepts are the packing number, the number of disjoint balls that fit in a space, and the metric entropy, the number of points that fit in a space when constrained to lie at some fixed minimum distance apart.

Definition

Let (M, d) be a metric space, let K be a subset of M, and let r be a positive real number. Let B_r(x) denote the ball of radius r centered at x. A subset C of M is an r-external covering of K if:

K\subseteq \bigcup _{x\in C}B_{r}(x)

.

In other words, for every $y\in K$ there exists $x\in C$ such that $d(x,y)\leq r$ .

If furthermore C is a subset of K, then it is an r-internal covering.

The external covering number of K, denoted $N_{r}^{\text{ext}}(K)$ , is the minimum cardinality of any external covering of K. The internal covering number, denoted $N_{r}^{\text{int}}(K)$ , is the minimum cardinality of any internal covering.

A subset P of K is a packing if $P\subseteq K$ and the set $\{B_{r}(x)\}_{x\in P}$ is pairwise disjoint. The packing number of K, denoted $N_{r}^{\text{pack}}(K)$ , is the maximum cardinality of any packing of K.

A subset S of K is r-separated if each pair of points x and y in S satisfies d(x, y) ≥ r. The metric entropy of K, denoted $N_{r}^{\text{met}}(K)$ , is the maximum cardinality of any r-separated subset of K.

Examples

The metric space is the real line $\mathbb {R}$ . $K\subset \mathbb {R}$ is a set of real numbers whose absolute value is at most $k$ . Then, there is an external covering of ${\textstyle \left\lceil {\frac {2k}{r}}\right\rceil }$ intervals of length $r$ , covering the interval $[-k,k]$ . Hence:
$N_{r}^{\text{ext}}(K)\leq {\frac {2k}{r}}$
The metric space is the Euclidean space $\mathbb {R} ^{m}$ with the Euclidean metric. $K\subset \mathbb {R} ^{m}$ is a set of vectors whose length (norm) is at most $k$ . If $K$ lies in a d-dimensional subspace of $\mathbb {R} ^{m}$ , then:[1]^: 337
$N_{r}^{\text{ext}}(K)\leq \left({\frac {2k{\sqrt {d}}}{r}}\right)^{d}$ .
The metric space is the space of real-valued functions, with the l-infinity metric. The covering number $N_{r}^{\text{int}}(K)$ is the smallest number $k$ such that, there exist $h_{1},\ldots ,h_{k}\in K$ such that, for all $h\in K$ there exists $i\in \{1,\ldots ,k\}$ such that the supremum distance between $h$ and $h_{i}$ is at most $r$ . The above bound is not relevant since the space is $\infty$ -dimensional. However, when $K$ is a compact set, every covering of it has a finite sub-covering, so $N_{r}^{\text{int}}(K)$ is finite.[2]^: 61

Properties

The internal and external covering numbers, the packing number, and the metric entropy are all closely related. The following chain of inequalities holds for any subset K of a metric space and any positive real number r.[3]
$N_{2r}^{\text{met}}(K)\leq N_{r}^{\text{pack}}(K)\leq N_{r}^{\text{ext}}(K)\leq N_{r}^{\text{int}}(K)\leq N_{r}^{\text{met}}(K)$
Each function except the internal covering number is non-increasing in r and non-decreasing in K. The internal covering number is monotone in r but not necessarily in K.

The following properties relate to covering numbers in the standard Euclidean space, $\mathbb {R} ^{m}$ :[1]^: 338

If all vectors in $K$ are translated by a constant vector $k_{0}\in \mathbb {R} ^{m}$ , then the covering number does not change.
If all vectors in $K$ are multiplied by a scalar $k\in \mathbb {R}$ , then:
for all $r$ : $N_{|k|\cdot r}^{\text{ext}}(k\cdot K)=N_{r}^{\text{ext}}(K)$
If all vectors in $K$ are operated by a Lipschitz function $\phi$ with Lipschitz constant $k$ , then:
for all $r$ : $N_{|k|\cdot r}^{\text{ext}}(\phi \circ K)\leq N_{r}^{\text{ext}}(K)$

Application to machine learning

Let $K$ be a space of real-valued functions, with the l-infinity metric (see example 3 above). Suppose all functions in $K$ are bounded by a real constant $M$ . Then, the covering number can be used to bound the generalization error of learning functions from $K$ , relative to the squared loss:[2]^: 61

\operatorname {Prob} \left[\sup _{h\in K}{\big \vert }{\text{GeneralizationError}}(h)-{\text{EmpiricalError}}(h){\big \vert }\geq \epsilon \right]\leq N_{r}^{\text{int}}(K)\,2\exp {-m\epsilon ^{2} \over 2M^{4}}

where $r={\epsilon \over 8M}$ and $m$ is the number of samples.

References

Shalev-Shwartz, Shai; Ben-David, Shai (2014). Understanding Machine Learning – from Theory to Algorithms. Cambridge University Press. ISBN 9781107057135.
Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet (2012). Foundations of Machine Learning. US, Massachusetts: MIT Press. ISBN 9780262018258.
Tao, Terence. "Metric entropy analogues of sum set theory". Retrieved 2 June 2014.

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[book14-1] Shalev-Shwartz, Shai; Ben-David, Shai (2014). Understanding Machine Learning – from Theory to Algorithms. Cambridge University Press. ISBN 9781107057135.

[book12-2] Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet (2012). Foundations of Machine Learning. US, Massachusetts: MIT Press. ISBN 9780262018258.

[3] Tao, Terence. "Metric entropy analogues of sum set theory". Retrieved 2 June 2014.