Geometric and combinatorial issues in data depth

Geometric and combinatorial issues in data depth

Greg AloupisUniversité Libre de Bruxelles

What is data depth?

A quantitative measurement of how central a

point is with respect to a data set.

Goals: to be able to rank data points, and to find the center of the data cloud.

Some geometric bivariate medians

Convex hull peeling (Tukey ’70s) ’85 Chazelle (nlogn)

Halfspace median (Tukey ’74) ’01 Langerman-Steiger O(nlog 3 n), ’03 Chan O(nlog n) -

randomized Oja median (Oja ’83)

’01 G.A.-Langerman-Soss-Toussaint O(nlog 3 n) Simplicial median (Liu ’88)

’01 ALST O(n4)

Convex hull peeling

Halfspace, simplicial and Oja depthsof a point in bivariate data set S

Each median is a point with max/min depth


(Tukey) halfspace depth: For every line through , count points

above/below. Return minimum number counted over all

lines.


(Tukey) halfspace depth: For every line through , count points above. Return minimum number counted over all

lines.


(Liu) simplicial depth: Count the closed triangles in S that contain .


(Liu) simplicial depth: Count the closed triangles in S that contain .

…etc


Oja depth: Sum areas of all triangles with vertices (,si ,sj)


Oja depth: Sum areas of all triangles with vertices (,si ,sj)

…etc


O(nlog n): Khuller-Mitchell ’89, Gil-Steiger-Wigderson ’92, Roussewu-Ruts ‘96

(nlog n): G.A.-Cortes-Gomez-Soss-Toussaint ’01, Langerman-Steiger ’01, G.A.-McLeish ’05

Issue 1:What is the complexity of computing the depth k of a point if k is known to be small/large? If the peel median has depth k>1 then can we

compute it faster? (GSW’92)

!!! this just in: simplicial depth in O(n+nlog (1+ k/n))

Elmasry-Elbassioni CCCG last week Is there a lower bound, sensitive to parameter k?

Something similar for halfspace depth? Current attempts for O(nlog k)

Remember, that horrible n4 result a few slides

back

Issue 2: (Improve) simplicial median computation

I : the set of line segments between pairs in S.

The simplicial median is on an intersection of two segments in I.

Easy observation

Outline of a method Preprocessing: O(n3) brute-force, actually O(n2)

Count number of points above/below each segment.

Compute depth of all points.

For each segment, sort all intersections with other segments.

O(n2log n). Calculate depth of each intersection in O(1)

time: O(n2)

Overall O(n4log n)

Constant time to update depth as we walk

Instead of sorting intersection points and processing each segment alone, we can use topological sweep.

The time complexity becomes O(n4) and the space used is O(n2).

Can we improve this? i.e. find some structure in this depth function

Conjecture: A point of maximum simplicial depth

can always be found on the intersection of two halving segments

(weak) experiments have not contradicted this

Desirable properties of data depth functions

Affine invariance (at the very least)

Robustness: Outliers should not influence the center.

Monotonicity: Center should move in “same” direction as

perturbations

monotonicity

Robustness to outliers breakdown point: fraction of data that must be

moved/added so that median is placed at infinity. Oja median: was considered to be robust, but

finally it was shown that the breakdown point can be near zero for certain configurations. (planar case) (Niinimaa,Oja,Tableman ’90)

simplicial median: don’t know. But the data point of maximum depth can be moved away with few corrupting points (GSW ’92) (planar case)

halfspace median: great! … 1/(d+1) (Donoho,Gasko ’92)

Robustness to outliers breakdown point: fraction of data that must

be moved/added so that median is placed at infinity.

Max breakdown = ½ In 1D, only the median is affine invariant,

monotonic and has max breakdown Is there such an estimator in higher

dimensions?

Issue 3How does the breakdown point depend on the depth of the median?

Convex peeling: breakdown is …zero, unless depth is linear (GSW’92)

Halfspace breakdown is higher (1/3) for centrosymmetric data distributions, where depth is roughly 1/2

Instead of 1/(d+1) So what can we say about other estimators?

For deepest point in plane For deepest data point

Issue 4:Non-strategic breakdownAll work so far involved carefully placing outliers

(erroneous or corrupt data), to move an estimator far away.(is corrupt data really placed carefully in practice?)

What about: • average outliers (random or evenly spaced placement) • strong breakdown (should work regardless of direction at

infinity)• special-case outliers (axis-parallel, or radial extension, or ?)

Issue 5:Computing/analyzing other estimators Projection outlyingness of (Donoho-Gasko ’92) Take max of the following, over all projections : |Median| / (median deviation from median)

Find an algorithm for the least outlying point.

Gil-Steiger-Wigderson: superposition of unit vectors to data points = v(ai) median is a (data) point with || v(ai) R || < 1 ??? computation in o(n2) ? Properties?

Zonoid depth, Delaunay depth …

Issue 6Points of high depth

A point w/ Tukey depth n/(d+1) is a centerpoint. Guaranteed to exist, by Helly’s thm. O(n) time computation (Jadhav-Mukhopadhyay ’94) Can be considered to be a median generalization.

¼ (n 3) simplicial depth 2/9 (n 3) (Boros-Furedi ’82) (in R2 , ignoring quadratic terms)

Can we compute a “high” depth point quickly? Tverberg points in R2 have depth 1/27 (n 3) and can

be computed in O(n) time. Anything better? Is there a point with “high” Oja depth? (normalized)

Things I may have mentioned in the abstract but forgot to include here: Is it faster to locate a deep point

without computing its depth? How many points have depth>k ? When do simplicial depth levels become

disconnected?

Geometric and combinatorial issues in data depth

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