1. Packing to fewer dimensions
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Packing to fewer dimensions
Paolo Ferragina
Dipartimento di Informatica
Università di Pisa

2. Speeding up cosine computation
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Speeding up cosine computation
What if we could take our vectors and “pack” them into fewer dimensions (say 50,000100) while preserving distances?
Now, O(nm) to compute cos(d,q) for all d
Then, O(km+kn) where k << n,m
Two methods:
“Latent semantic indexing”
Random projection

3. Briefly LSI is data-dependent Random projection is data-independent
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Briefly
LSI is data-dependent
Create a k-dim subspace by eliminating redundant axes
Pull together “related” axes – hopefully
car and automobile
Random projection is data-independent
Choose a k-dim subspace that guarantees good stretching properties with high probability between any pair of points.
What about polysemy ?

4. Latent Semantic Indexing
Sec. 18.4
Latent Semantic Indexing
courtesy of Susan Dumais

5. Notions from linear algebra
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Notions from linear algebra
Matrix A, vector v
Matrix transpose (At)
Matrix product
Rank
Eigenvalues l and eigenvector v: Av = lv
Example

6. Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Overview of LSI
Pre-process docs using a technique from linear algebra called Singular Value Decomposition
Create a new (smaller) vector space
Queries handled (faster) in this new space

7. Singular-Value Decomposition
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Singular-Value Decomposition
Recall m  n matrix of terms  docs, A.
A has rank r  m,n
Define term-term correlation matrix T=AAt
T is a square, symmetric m  m matrix
Let P be m  r matrix of r eigenvectors of T
Define doc-doc correlation matrix D=AtA
D is a square, symmetric n  n matrix
Let R be n  r matrix of r eigenvectors of D

8. A’s decomposition Rt S P A
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
A’s decomposition
Given P (for T, m  r) and R (for D, n  r) formed by orthonormal columns (unit dot-product)
It turns out that A = PSRt
Where S is a diagonal matrix with the eigenvalues of T=AAt in decreasing order.
mn =
mr
rn
rr Rt S P A

9. Dimensionality reduction
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Dimensionality reduction
For some k << r, zero out all but the k biggest eigenvalues in S [choice of k is crucial]
Denote by Sk this new version of S, having rank k
Typically k is about 100, while r (A’s rank) is > 10,000
document k k k =
useless due to 0-col/0-row of Sk r S Sk Rt P Ak A
r x n
k x n
m x r
m x k

10. Guarantee Ak is a pretty good approximation to A:
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Guarantee
Ak is a pretty good approximation to A:
Relative distances are (approximately) preserved
Of all m  n matrices of rank k, Ak is the best approximation to A wrt the following measures:
minB, rank(B)=k ||A-B||2 = ||A-Ak||2 = sk+1
minB, rank(B)=k ||A-B||F2 = ||A-Ak||F2 = sk+12+ sk sr2
Frobenius norm ||A||F2 = s12+ s sr2

11. Reduction We use Xk to define how to project A and Q:
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
R,P are formed by
orthonormal eigenvectors
of the matrices D,T
Reduction
Xk = Sk Rt is the doc-matrix k x n, hence reduced to k dim
Since we are interested in doc/q correlation, we consider:
D=At A =(P S Rt)t (P S Rt) = (SRt)t (SRt)
Approx S with Sk, thus get At A  Xkt Xk (both are n x n matr.)
We use Xk to define how to project A and Q:
Xk = Sk Rt , we have Pkt A = Sk Rt = Xk
Since Xk plays the role of A, its columns are the projected docs, hence it is enough to multiply Q by Pkt to get the projected query, O(km) time
Cost of sim(q,d), for all d, is O(kn+km) instead of O(mn)

12. Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Which are the concepts ?
c-th concept = c-th row of Pkt (which is k x m)
Denote it by Pkt [c], whose size is m = #terms
Pkt [c][i] = strength of association between c-th concept and i-th term
Projected document: d’j = Pkt dj
d’j[c] = strenght of concept c in dj
Projected query: q’ = Pkt q
q’ [c] = strenght of concept c in q

13. Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Random Projections
Paolo Ferragina
Dipartimento di Informatica
Università di Pisa
Slides only !

14. An interesting math result
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
An interesting math result
Lemma (Johnson-Linderstrauss, ‘82)
Let P be a set of n distinct points in m-dimensions.
Given e > 0, there exists a function f : P  IRk such that for every pair of points u,v in P it holds:
(1 - e) ||u - v||2 ≤ ||f(u) – f(v)||2 ≤ (1 + e) ||u-v||2
Where k = O(e-2 log n)
f() is called JL-embedding
Setting v=0 we also get a bound on f(u)’s stretching!!!

15. What about the cosine-distance ?
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
What about the cosine-distance ?
f(u)’s, f(v)’s stretching
substituting formula above
for ||u-v||2

16. How to compute a JL-embedding?
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
How to compute a JL-embedding?
If we set the projection matrix P = pi,j as a random m x k matrix, where its components are independent random variables with one of the following two distributions:
E[ri,j] = 0
Var[ri,j] = 1

17. Finally... Random projections hide large constants
Prof. Paolo Ferragina, Algoritmi per "Information Retrieval"
Finally...
Random projections hide large constants
k  (1/e)2 * log n, so k may be large…
it is simple and fast to compute
LSI is intuitive and may scale to any k
optimal under various metrics
but costly to compute, do exist good libraries