1. Computer examples Tenenbaum, de Silva, Langford “A Global Geometric Framework for Nonlinear Dimensionality Reduction”

2. Statue face database 698 64x64 grayscale images 2 mins, 12 secs on a ~600 (?) MHz PIII

3. The computed manifold

4. Testing the sensibility of the manifold coordinates One test you could do: 1.Sort all faces according to first manifold coordinate (“left-right”) 2.View them in order 3.See if the face makes a monotonic progression from left to right

5. Testing the sensibility of the manifold coordinates

6. Semantic consistency of a dimension value deteriorates between points that are far away on the manifold. Some consecutive frames: Well-lit faces are turning left, poorly-lit faces are turning left.

7. Testing the sensibility of the manifold coordinates Semantic consistency of a dimension value deteriorates between points that are far away on the manifold. Explanations: Geodesic distance on the manifold is approximated by shortest-path distance in a neighbor graph.  Sparse neighbor graphs result in high distance error for points far away on the graph.

8. Testing the sensibility of the manifold coordinates Geodesic distance approximator can’t be perfect in the face of sparse data

9. Testing the sensibility of the manifold coordinates The test expected this face:

10. Testing the sensibility of the manifold coordinates …to be a bit more left-facing than this face:

11. Traversing the manifold Collapsing the manifold to one dimension isn’t the way to use it. Try tracing one dimension while keeping the other dimensions from jumping around too much.

12. Traversing the manifold Algorithm used: Sort images by “left-right” coord as before Draw a smooth line through the manifold Only add images that are within a certain manifold distance D from this line.

13. Traversing the manifold

14. Make a better picture to illustrate that it’s more of a band than a path

15. Traversing the manifold Fix oval so that it encricles the middle three rows of faces instead

16. Traversing the manifold D = 20 (Half the range of the “up-down” dimension)

17. Traversing the manifold (D = 30)

18. Traversing the manifold D = 40 (using 80% of the faces)

19. Traversing the manifold D = 50 (using 98% of the faces)

20. Comparison to LLE Run both algorithms on 100 of the statue faces (64 x 64 pixels) Isomap LLE

21. Comparison to LLE Running time for 100 64x64 images: LLE: 5 secs Isomap: 1.39 secs

22. Comparison to LLE The collapsing-to-primary-dimension-test:

23. Comparison to LLE Uh… the collapsing-to-second-dimension-test

24. Comparison to LLE The horizontal manifold traversal test (7 frames)

25. Comparison to LLE LLE: once manifold is computed, meaningful paths through it need to be found.

26. Weakness under translation Images with a common background and a single translating object will have a rough time with pixel differences.

27. Weakness under translation Uniform translation, no overlap Input images: Output images:

28. Weakness under translation Uniform translation, 1-column overlap Input images: Output images:

29. Weakness under translation Uniform translation, 1-column overlap

30. End