PCA Extension

1
PCA Extension

• By Jonash

2
Outline

• Robust PCA
• Generalized PCA
• Clustering points on a line
• Clustering lines on a plane
• Clustering hyperplanes in a space

3
Robust PCA

• Rrbust Principal Component Analysis for Computer
  Vision
• Fernando De la Torre
• Mochael J. Black
• CS, Brown University

4
PCA is Least-Square Fit
5
PCA is Least-Square Fit
6
Robust Statistics

• Recover the best fit for the majority of the data
• Detect and reject outliers

7
Robust PCA
8
Robust PCA
9
Robust PCA

• Training images

10
Robust PCA

• Naïve PCA
• Simply reject
• Robust PCA

11
RPCA

• In traditional PCA, we minimize
• Sni 0 (di B BT di)2 Sni 0 (di - Bci)2
• EM PCA Lims -gt 0(D BC s2I)
• E-step C (BTB)-1BTD
• M-step B DCT(CCT)-1

BBTdi
di
B
BTdi ci
12
RPCA

• Xu and Yuille 1995 tries to minimize
• Sni 1 V i(di B ci)2 n(1-Vi)
• Hard to solve (continuous discrete)

13
RPCA

• Gabriel and Zamir 1979 tries to minimize
• Sni 1 Sdp 1 wpi(dpi Bci)2
• Impratical for high dimension
• Low rank approximation of matrices by least
  squares with any choice of weights 1979

14
RPCA

• Idea is to use a robust function ?
• Geman-McClure? (x,s) x2/(x2 s2)
• Sni 1 Sdp 1 ? (dpiµp Skj 1 bpjcji), sp
• Approximated by local quadratic function
• Use gradient descent
• The rest is nothing but heuristics

15
RPCA
16
Robust PCA - Experiment

• 256 training images (120x160)
• Obtain 20 RPCA basis
• 3 hrs on 900MHz Pentinum III in Matlab

17
Outline

• Robust PCA
• Generalized PCA
• Clustering points on a line
• Clustering lines on a plane
• Clustering hyperplanes in a space

18
Generalized PCA

• Generalized Principal Component Analysis
• Rene Vidal
• Yi Ma
• Shankar Sastry
• UC Berkley and UIUC

19
GPCA
20
GPCA Example 1
21
GPCA Example 2
22
GPCA Example 3
23
GPCA Goals

• of subspaces and their dimension
• Basis for subspace
• Segmentation of data

24
GPCA Ideas

• Union of subspaces certain polynomials

25
Outline

• Robust PCA
• Generalized PCA
• Clustering points on a line
• Clustering lines on a plane
• Clustering hyperplanes in a space

26
GPCA 1D Case
27
GPCA 1D Case Contd
28
GPCA 1D Case Contd
MN n1
unknowns
To have a unique solution, rank(Vn) n Mn- 1
29
GPCA 1D Example

• n 2 groups
• pn(x) ( x µ1) ( x µ2)
• No polynomial of degree 1
• Infinite polynomial of degree 3
• pn(x) x2 c1x c2 gt Polynomial factor

30
Outline

• Robust PCA
• Generalized PCA
• Clustering points on a line
• Clustering lines on a plane
• Clustering hyperplanes in a space

31
GPCA 2D Case

• L j X x, yT bj1x bj2y 0
• (b11x b12y 0) or (b21x b22y 0)

32
GPCA 2D Case Contd

• (b11x b12y 0) or (b21x b22y 0)
• Pn(x) (b11x b12y)(bn1x bn2y) 0
• Sck xn-k yk

33
GPCA 2D Case Contd

• Take n 2 for example
• p2(x) (b11x b12y)(b21x b22y)
• ?p2(x) (b21x b22y)b1 (b11x b12y)b2
• , bj bj1, bj2 T
• if x L1, then ?p2(x) b1, otherwise b2

34
GPCA 2D Case Contd

• Given that yj e Lj, the normal vector of Lj is
  bj ?pn(yj)
• 3 things
• Determine n as min j rank(Vj) j
• Solve cn for Vncn 0
• Find normal vector bj

35
Outline

• Robust PCA
• Generalized PCA
• Clustering points on a line
• Clustering lines on a plane
• Clustering hyperplanes in a space

36
GPCA Hyperplanes

• Still assume d1 dn d D 1
• Sj bjTx bj1x1 bj2x2 bjDxD 0

37
GPCA Hyperplanes
MN C(Dn-1, D)
38
GPCA Hyperplanes
39
GPCA Hyperplanes

• Since we know n, we can solve for ck
• ck gt bk by ?pn(x)
• If we know yj on each Sj, finding bj will be easy

40
GPCA Hyperplanes

• One point yj on each hyperplane Sj
• Consider a random line L t v x0
• Obtain yj by intersecting L and Sj
• yj tj v x0
• Find roots tj by Pn(t v xo)

41
GPCA Hyperplanes

• Summarize
• We want to find n to solve for c
• To get b (normal) for each S, find ?pn(x)
• To get label j, solve pn(yj tj v x0) 0

42
(No Transcript)
43

• One More Thing

44
One More Thing

• Previously we assume d1 dn D 1
• Actually we cannot assume that
• Please read section 4.2 4.3 by yourself
• Discuss how to recursively reduce dimension

45
(No Transcript)