subspace embeddings for the l1 norm with applications christian sohler david woodruff tu dortmund...

Subspace Embeddings for the L1 norm with Applications

Christian Sohler David WoodruffTU Dortmund IBM Almaden

Subspace Embeddings for the L1 norm with Applications

to...

Robust Regression and Hyperplane Fitting

3

Outline

Massive data sets Regression analysis Our results Our techniques Concluding remarks

4

Massive data sets

Examples Internet traffic logs Financial data etc.

Algorithms Want nearly linear time or less Usually at the cost of a randomized approximation

5

Regression analysis

Regression Statistical method to study dependencies between variables in the

presence of noise.

6

Regression analysis

Linear Regression Statistical method to study linear dependencies between variables in the

presence of noise.

7

Regression analysis

Linear Regression Statistical method to study linear dependencies between variables in the

presence of noise.

Example Ohm's law V = R ∙ I

8

Regression analysis

Linear Regression Statistical method to study linear dependencies between variables in the

presence of noise.

Example Ohm's law V = R ∙ I Find linear function that best

fits the data

9

Regression analysis

Linear Regression Statistical method to study linear dependencies between variables in the

presence of noise.

Standard Setting One measured variable b A set of predictor variables a ,…, a Assumption:

b = x + a x + … + a x + is assumed to be noise and the xi are model parameters we want to learn

Can assume x0 = 0

Now consider n measured variables

1 d

1 1 d d0

10

Regression analysis

Matrix formInput: nd-matrix A and a vector b=(b1,…, bn)

n is the number of observations; d is the number of predictor variables

Output: x* so that Ax* and b are close

Consider the over-constrained case, when n À d Can assume that A has full column rank

11

Regression analysis

Least Squares Method

Find x* that minimizes (bi – <Ai*, x*>)²

Ai* is i-th row of A

Certain desirable statistical properties

Method of least absolute deviation (l1 -regression)

Find x* that minimizes |bi – <Ai*, x*>|

Cost is less sensitive to outliers than least squares

12

Regression analysis

Geometry of regression We want to find an x that minimizes |Ax-b|1

The product Ax can be written as

A*1x1 + A*2x2 + ... + A*dxd

where A*i is the i-th column of A

This is a linear d-dimensional subspace The problem is equivalent to computing the point of the column space of A

nearest to b in l1-norm

13

Regression analysis

Solving l1 -regression via linear programming

Minimize (1,…,1) ∙ ( + ) Subject to:

A x = b

, ≥ 0

Generic linear programming gives poly(nd) time

Best known algorithm is nd5 log n + poly(d/ε) [Clarkson]

+ -

+ -

+ -

14

Our Results

A (1+ε)-approximation algorithm for l1-regression problem

Time complexity is nd1.376 + poly(d/ε)

(Clarkson’s is nd5 log n + poly(d/ε))

First 1-pass streaming algorithm with small space

(poly(d log n /ε) bits)

Similar results for hyperplane fitting

15

Outline

Massive data sets Regression analysis Our results Our techniques Concluding remarks

16

Our Techniques

Notice that for any d x d change of basis matrix U,

minx in Rd |Ax-b|1 = minx in Rd |AUx-b|1

17

Our Techniques

Notice that for any y 2 Rd,

minx in Rd |Ax-b|1 = minx in Rd |Ax-b+Ay|1

We call b-Ay the “residual”, denoted b’, and so

minx in Rd |Ax-b|1 = minx in Rd |Ax-b’|1

18

Rough idea behind algorithm of Clarkson

Compute poly(d)-approximation

Compute poly(d)-approximation

Compute well-conditionedbasis

Compute well-conditionedbasis

Sample rows from the well-conditioned basis and the residual of the poly(d)-

approximation

Sample rows from the well-conditioned basis and the residual of the poly(d)-

approximation

Solve l1-regression on the sample, obtaining vector x, and output xSolve l1-regression on the sample, obtaining vector x, and output x

Find y such that|Ay-b|1 · poly(d) minx in Rd |Ax-b|1

Let b’ = b-Ay be the residual

Find y such that|Ay-b|1 · poly(d) minx in Rd |Ax-b|1

Let b’ = b-Ay be the residual

Find a basis U so that for all x in Rd,

|x|1/poly(d) · |AUx|1 · poly(d) |x|1

Find a basis U so that for all x in Rd,

|x|1/poly(d) · |AUx|1 · poly(d) |x|1

minx in Rd |Ax-b|1 = minx in Rd |AUx – b’|1

Sample poly(d/ε) rows of AU◦b’ proportional to their l1-norm.

minx in Rd |Ax-b|1 = minx in Rd |AUx – b’|1

Sample poly(d/ε) rows of AU◦b’ proportional to their l1-norm.

Takes nd5 log n timeTakes nd5 log n time

Takes nd timeTakes nd time

Takes nd5 log n timeTakes nd5 log n time

Takes poly(d/ε) timeTakes poly(d/ε) timeNow generic linear programming is efficient

Now generic linear programming is efficient

19

Our Techniques

Suffices to show how to quickly compute

1. A poly(d)-approximation

2. A well-conditioned basis

20

Our main theorem

Theorem There is a probability space over (d log d) n matrices R such that for any

nd matrix A, with probability at least 99/100 we have for all x:

|Ax|1 ≤ |RAx|1 ≤ d log d ∙ |Ax|1

Embedding is linear is independent of A preserves lengths of an infinite number of vectors

21

Application of our main theorem

Computing a poly(d)-approximation

Compute RA and Rb

Solve x’ = argminx |RAx-Rb|1

Main theorem applied to A◦b implies x’ is a d log d – approximation

RA, Rb have d log d rows, so can solve l1-regression efficiently

Time is dominated by computing RA, a single matrix-matrix product

22

Application of our main theorem

Computing a well-conditioned basis

1. Compute RA

2. Compute U so that RAU is orthonormal (in the l2-sense)

3. Output AU

AU is well-conditioned because:

|AUx|1 · |RAUx|1 · (d log d)1/2 |RAUx|2 = (d log d)1/2 |x|2 · (d log d)1/2 |x|1and

|AUx|1 ¸ |RAUx|1/(d log d) ¸ |RAUx|2/(d log d) = |x|2/(d log d) ¸ |x|1 /(d3/2 log d)

Life is really simple!

Life is really simple!

Time dominated by computing RA and AU, two matrix-matrix products

Time dominated by computing RA and AU, two matrix-matrix products

23

Application of our main theorem

It follows that we get an nd1.376 + poly(d/ε) time algorithm for (1+ε)-approximate l1-regression

24

What’s left?

We should prove our main theorem Theorem: There is a probability space over (d log d) n matrices R such that for any

nd matrix A, with probability at least 99/100 we have for all x:

|Ax|1 ≤ |RAx|1 ≤ d log d ∙ |Ax|1

R is simple The entries of R are i.i.d. Cauchy random variables

25

Cauchy random variables

pdf(z) = 1/(π(1+z)2) for z in (-1, 1)

Infinite expectation and variance

1-stable: If z1, z2, …, zn are i.i.d. Cauchy, then for a 2 Rn,

a1¢z1 + a2¢z2 + … + an¢zn » |a|1¢z, where z is Cauchy

z

26

Proof of main theorem

By 1-stability, For all rows r of R,

<r, Ax> » |Ax|1¢Z,

where Z is a Cauchy

RAx » (|Ax|1 ¢ Z1, …, |Ax|1 ¢ Zd log d),

where Z1, …, Zd log d are i.i.d. Cauchy

|RAx|1 = |Ax|1 i |Zi|

The |Zi| are half-Cauchy

i |Zi| = (d log d) with probability 1-exp(-d) by Chernoff

ε-net argument on {Ax | |Ax|1 = 1} shows |RAx|1 = |Ax|1¢(d log d) for all x

Scale R by 1/(d log d)

i |Zi| = (d log d) with probability 1-exp(-d) by Chernoff

ε-net argument on {Ax | |Ax|1 = 1} shows |RAx|1 = |Ax|1¢(d log d) for all x

Scale R by 1/(d log d)

But i |Zi| is heavy-tailed But i |Zi| is heavy-tailed

z

/ (d log d)

27

Proof of main theorem

i |Zi| is heavy-tailed, so |RAx|1 = |Ax|1 i |Zi| / (d log d) may be large

Each |Zi| has c.d.f. asymptotic to 1-Θ(1/z) for z in [0, 1)

No problem!

We know there exists a well-conditioned basis of A We can assume the basis vectors are A*1, …, A*d

|RA*i|1 » |A*i|1 ¢ i |Zi| / (d log d)

With constant probability, i |RA*i|1 = O(log d) i |A*i|1

28

Proof of main theorem

Suppose i |RA*i|1 = O(log d) i |A*i|1 for well-conditioned basis A*1, …, A*d

We will use the Auerbach basis which always exists: For all x, |x|1 · |Ax|1 i |A*i|1 = d

I don’t know how to compute such a basis, but it doesn’t matter!

i |RA*i|1 = O(d log d)

|RAx|1 · i |RA*i xi| · |x|1 i |RA*i|1 = |x|1O(d log d) = O(d log d) |Ax|1

Q.E.D.

29

Main Theorem

Theorem

There is a probability space over (d log d) n matrices R such that for any nd matrix A, with probability at least 99/100 we have for all x:

|Ax|1 ≤ |RAx|1 ≤ d log d ∙ |Ax|1

30

Outline

Massive data sets Regression analysis Our results Our techniques Concluding remarks

31

Regression for data streams

Streaming algorithm given additive updates to entries of A and b Pick random matrix R according to the distribution of main theorem Maintain RA and Rb during the stream Find x'that minimizes |RAx'-Rb|1 using linear programming

Compute U so that RAU is orthonormal

The hard thing is sampling rows from AU◦b’ proportional to their norm Do not know U, b’ until end of stream Surpisingly, there is still a way to do this in a single pass by treating U, x’ as

formal variables and plugging them in at the end Uses a noisy sampling data structure Omitted from talk

Entries of R do not need to be independent

Entries of R do not need to be independent

32

Hyperplane Fitting

Reduces to d invocations of l1-regression

Given n points in Rd, find hyperplane minimizing sum of l1-distances ofpoints to the hyperplane

33

Conclusion

Main results

Efficient algorithms for l1-regression and hyperplane fitting

nd1.376 time improves previous nd5 log n running time for l1-regression

First oblivious subspace embedding for l1