1. Dimensionality Reduction
CS 685: Special Topics in Data Mining
Jinze Liu

2. Overview What is Dimensionality Reduction?
Simplifying complex data
Using dimensionality reduction as a Data Mining “tool”
Useful for both “data modeling” and “data analysis”
Tool for “clustering” and “regression”
Linear Dimensionality Reduction Methods
Principle Component Analysis (PCA)
Multi-Dimensional Scaling (MDS)
Non-Linear Dimensionality Reduction

3. What is Dimensionality Reduction?
Given N objects, each with M measurements, find the best D-dimensional parameterization
Goal: Find a “compact parameterization” or “Latent Variable” representation
Given N examples of find where
Underlying assumptions to DimRedux
Measurements over-specify data, M > D
The number of measurements exceed the number of “true” degrees of freedom in the system
The measurements capture all of the significant variability
                  

4. Uses for DimRedux Build a “compact” model of the data
Compression for storage, transmission, & retrieval
Parameters for indexing, exploring, and organizing
Generate “plausible” new data
Answer fundamental questions about data
What is its underlying dimensionality? How many degrees of freedom are exhibited? How many “latent variables”?
How independent are my measurements?
Is there a projection of my data set where important relationships stand out?

5. DimRedux in Data Modeling
Data Clustering - Continuous to Discrete
The curse of dimensionality: the sampling density is proportional to N1/p.
Need a mapping to a lower-dimensional space that preserves “important” relations
Regression Modeling – Continuous to Continuous
A functional model that generates input data
Useful for interpolation
Embedding Space

6. Today’s Focus Linear DimRedux methods “Linear” Assumption
PCA – Pearson (1901); Hotelling (1935)
MDS – Torgerson (1952), Shepard (1962)
“Linear” Assumption
Data is a linear function of the parameters (latent variables)
Data lies on a linear (Affine) subspace
where the matrix M is m x d

7. PCA: What problem does it solve?
Minimizes “least-squares” (Euclidean) error
The D-dimensional model provided by PCA has the smallest Euclidean error of any D-parameter linear model.
where is the model predicted by the D-dimensional PCA.
Projects data s.t. the variance is maximized
Find an optimal “orthogonal” basis set for describing the given data

8. Principle Component Analysis
Also known to engineers as the Karhunen-Loéve Transform (KLT)
Rotate data points to align successive axes with directions of greatest variance
Subtract mean from data
Normalize variance along each direction, and reorder according to the variance magnitude from high to low
Normalized variance direction = principle component
Eigenvectors of system’s Covariance Matrix permute to order eigenvectors in descending order

9. Simple PCA Example Simple 3D example >> x = rand(2, 500);
>> z = [1,0; 0,1; -1,-1] * x + [0;0;1] * ones(1, 500);
>> m = (100 * rand(3,3)) * z + rand(3, 500);
>> scatter3(m(1,:), m(2,:), m(3,:), 'filled');

10. Simple PCA Example (cont)
>> mm = (m- mean(m')' * ones(1, 500));;
>> [E,L] = eig(cov(mm ‘ ));
>> E
E =
>> L
L =
>> newm = E’ * (m - mean(m’)’' * ones(1, 500));
>> scatter3(newm(1,:), newm(2,:), newm(3,:), 'filled');
axis([-50,50, -50,50, -50,50]);

11. Simple PCA Example (cont)

12. PCA Applied to Reillumination
Illumination can be modeled as an additive linear system. ) ( R i xy w

13. Simulating New Lighting
We can simulate the appearance of a model under new illumination by combining images taken from a set of basis lights
We can then capture real-world lighting and use it to modulate our basis lighting functions

14. Problems There are too many basis lighting functions
These have to be stored in order to use them
The resulting lighting model can be huge, in particular when representing high frequency lighting
Lighting differences can be very subtle
The cost of modulation is excessive
Every basis image must be scaled and added together
Each image requires a high-dynamic range
Is there a more compact representation?
Yes, use PCA.

15. PCA Applied to Illumination
More than 90% variance is captured in the first five principle components
Generate new illumination by combining only 5 basis images V0
for n lights

16. Results Video

17. Results Video

18. Results Video

19. MDS: What problem does it solve?
Takes as input a dissimilarity matrix M, containing pairwise dissimilarities between N-dimensional data points
Finds the best D-dimensional linear parameterization compatible with M
(in other words, outputs a projection of data in D-dimensional space where the pairwise distances match the original dissimilarities as faithfully as possible)

20. Multidimensional Scaling (MDS)
Dissimilarities can be metric or non-metric
Useful when absolute measurements are unavailable; uses relative measurements
Computation is invariant to dimensionality of data

21. An example: map of the US
Given only the distance between a bunch of cities

22. An example: map of the US
MDS finds suitable coordinates for the points of the specified dimension.

23. MDS Properties Parameterization is not unique – Axes are meaningless
Not surprising since Euclidean transformations and reflections preserve distances between points
Useful for visualizing relationships in high dimensional data.
Define a dissimilarity measure
Map to a lower-dimensional space using MDS
Common preprocess before cluster analysis
Aids in understanding patterns and relationships in data
Widely used in marketing and psychometrics

24. Dissimilarities
Dissimilarities are distance-like quantities that satisfy the following conditions:
A dissimilarity is metric if, in addition, it satisfies:
“The triangle inequality”

25. Relating MDS to PCA Special case: when distances are Euclidean
PCA = eigendecomposition of covariance matrix MTM
Convert the pair-wise distance matrix to the covariance matrix

26. How to get MTM from Euclidean Pair-wise Distances
Law of cosines k j
Definition of a dot product
Eigendecomposition on b to get VSVT
VS1/2 = matrix of new coordinates

27. Algebraically… So we “centered” the matrix The “Matrix Average”
The distance between points pi and pj
The *Column Average* the average distance that a given point is from pj
The “Matrix Average”
The *Row Average* the average distance that a given point is from pi
So we “centered” the matrix

28. MDS Mechanics
Given a Dissimilarity matrix, D, the MDS model is computed as follows:
Where, H, the so called “centering” matrix, is a scaled identity matrix computed as follows:
MDS coordinates given by (in order of decreasing :

29. MDS Stress
The residual variance of B (i.e. the sum of the remaining eigenvalues) indicate the goodness of fit for the selected d-dimensional model
This term is often called MDS “stress”
Examining the residual variance gives an indication of the inherent dimensionality

30. Reflectance Modeling Example
The top row of white, grey, and black balls have the same “physical” reflectance parameters, however, the bottom row is “perceptually” more consistent.
From Pellacini, et. al. “Toward a Psychophysically-Based Light Reflection Model for Image Synthesis,” SIGGRAPH 2000
Objective – Find a perceptually meaningful parameterization for reflectance modeling

31. Reflectance Modeling Example
User Task – Subjects were presented with 378 pairs of rendered spheres an asked to rate their difference in “glossiness” on a scale of 0 (no difference) to 100.
A dissimilarity 27 x 27 dissimilarity matrix was constructed and MDS applied

32. Reflectance Modeling Example
Parameters of a 2D embedding space were determined
Two axes of “gloss” were established

33. Limitations of Linear methods
What if the data does not lie within a linear subspace?
Do all convex combinations of the measurements generate plausible data?
Low-dimensional non-linear Manifold embedded in a higher dimensional space
Next time: Nonlinear Dimensionality Reduction

34. Nonlinear Dimensionality Reduction
Many data sets contain essential nonlinear structures that invisible to PCA and MDS
Resorts to some nonlinear dimensionality reduction approaches.
Kernel methods
Depend on the kernels
Most kernels are not data dependent

35. Nonlinear Approaches- Isomap
Josh. Tenenbaum, Vin de Silva, John langford 2000
Constructing neighbourhood graph G
For each pair of points in G, Computing shortest path distances ---- geodesic distances.
Use Classical MDS with geodesic distances.
Euclidean distance Geodesic distance

36. Sample points with Swiss Roll
Altogether there are 20,000 points in the “Swiss roll” data set. We sample 1000 out of 20,000.

37. Construct neighborhood graph G
K- nearest neighborhood (K=7)
DG is 1000 by 1000 (Euclidean) distance matrix of two neighbors (figure A)

38. Compute all-points shortest path in G
Now DG is 1000 by 1000 geodesic distance matrix of two arbitrary points along the manifold (figure B)

39. Use MDS to embed graph in Rd
Find a d-dimensional Euclidean space Y (Figure c) to preserve the pariwise diatances.

40. The Isomap algorithm

41. PCA, MD vs ISOMAP

42. Isomap: Advantages Nonlinear Globally optimal
Still produces globally optimal low-dimensional Euclidean representation even though input space is highly folded, twisted, or curved.
Guarantee asymptotically to recover the true dimensionality.

43. Isomap: Disadvantages
May not be stable, dependent on topology of data
Guaranteed asymptotically to recover geometric structure of nonlinear manifolds
As N increases, pairwise distances provide better approximations to geodesics, but cost more computation
If N is small, geodesic distances will be very inaccurate.

44. Applications Isomap and Nonparametric Models of Image Deformation
LLE and Isomap Analysis of Spectra and Colour Images
Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences
Mining the structural knowledge of high-dimensional medical data using isomap
Isomap Webpage:

45. Summary Linear dimensionality reduction tools are widely used for
Data analysis
Data preprocessing
Data compression
PCA transforms the measurement data s. t. successive directions of greatest variance are mapped to orthogonal axis directions (bases)
An D-dimensional embedding space (parameterization) can be established by modeling the data using only the first d of these basis vectors
Residual modeling error is the sum of the remaining eigenvalues

46. Summary (cont)
MDS finds a d-dimensional parameterization that best preserves a given dissimilarity matrix
Resulting model can be Euclidean transformed to align data with a more intuitive parameterization
An D-dimensional embedding spaces (parameterization) are established by modeling the data using only the first d coordinates of the scaled eigenvectors
Residual modeling error (MDS stress) is the sum of the remaining eigenvalues
If Euclidean metric dissimilarity matrix is used for MDS the resulting d-dimensional model will match the PCA weights for the same dimensional model