[SOLVED] MATH5473 Homework 6-Manifold Learning

1. Order the faces: The following dataset contains 33 faces of the same person (Y ∈ R^112×92×33) in different angles, https://yao-lab.github.io/data/face.mat

You may create a data matrix X ∈ R^n×p where n = 33,p = 112 × 92 = 10304 (e.g.

X=reshape(Y,[10304,33])’; in matlab).

• Explore the MDS-embedding of the 33 faces on top two eigenvectors: order the facesaccording to the top 1st eigenvector and visualize your results with figures.
• Explore the ISOMAP-embedding of the 33 faces on the k = 5 nearest neighbor graph and compare it against the MDS results. Note: you may try Tenenbaum’s Matlab code https://yao-lab.github.io/data/isomapII.m
• Explore the LLE-embedding of the 33 faces on the k = 5 nearest neighbor graph and compare it against ISOMAP. Note: you may try the following Matlab code https://yao-lab.github.io/data/lle.m

2. Manifold Learning: The following codes by Todd Wittman contain major manifold learning algorithms talked on class.

http://math.stanford.edu/~yuany/course/data/mani.m

Precisely, eight algorithms are implemented in the codes: MDS, PCA, ISOMAP, LLE, Hessian Eigenmap, Laplacian Eigenmap, Diffusion Map, and LTSA. The following nine examples are given to compare these methods,

• Swiss roll;
• Swiss hole;
• Corner Planes;
• Punctured Sphere;
• Twin Peaks;
• 3D Clusters;
• Toroidal Helix;
• Gaussian;
• Occluded Disks.

1

Homework 6. Manifold Learning                                                                                                                                              2

Run the codes for each of the nine examples, and analyze the phenomena you observed.

*Moreover if possible, play with t-SNE using scikit-learn manifold package:

http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html or any other implementations collected at

http://lvdmaaten.github.io/tsne/

3. Nystr¨om method: In class, we have shown that every manifold learning algorithm can be regarded as Kernel PCA on graphs: (1) given N data points, define a neighborhood graph with N nodes for data points; (2) construct a positive semidefinite kernel K; (3) pursue spectral decomposition of K to find the embedding (using top or bottom eigenvectors).

However, this approach might suffer from the expensive computational cost in spectral decomposition of K if N is large and K is non-sparse, e.g. ISOMAP and MDS.

To overcome this hurdle, Nystr¨om method leads us to a scalable approach to compute eigenvectors of low rank matrices. Suppose that an N-by-N positive semidefinite matrix

0 admits the following block partition

 .                                                                          (1)

where A is an n-by-n block. Assume that A has the spectral decomposition A = UΛU^T,

Λ = diag(λ_i) (λ₁ ≥ λ₂ ≥ …λ_k > λ_k+1 = … = 0) and U = [u₁,…,u_n] satisfies U^TU = I.

• Assume that K = XX^T for some X = [X₁;X₂] ∈ R^N×k with the block X₁ ∈ R^n×k.

Show that X₁ and X₂ can be decided by:

1/2

X₁ = U_kΛ_k ,                                                                            (2)

X2 = BTUkΛ−k 1/2,                                                                      (3)

where U_k = [u₁,…,u_k] consists of those k columns of U corresponding to top k eigenvalues λ_i (i = 1,…,k).

• Show that for general 0, one can construct an approximation from (2) and (3),

 .                                                                     (4)

where , and  denoting the Moore-

Penrose (pseudo-) inverse of A. Therefore kK^ˆ − Kk_F = kC − B^TA^†Bk_F. Here the matrix C − B^TA^†B =: K/A is called the (generalized) Schur Complement of A in K.

• Explore Nystr¨om method on the Swiss-Roll dataset (http://yao-lab.github.io/data/ mat contains 3D-data X; http://yao-lab.github.io/data/swissroll. m is the matlab code) with ISOMAP. To construct the block A, you may choose either of the following:

n random data points;

Homework 6. Manifold Learning                                                                                                                                              3

*n landmarks as minimax k-centers (https://yao-lab.github.io/data/kcenter. m);

Some references can be found at:

[dVT04] Vin de Silva and J. B. Tenenbaum, “Sparse multidimensional scaling using landmark points”, 2004, downloadable at http://pages.pomona.edu/~vds04747/ public/papers/landmarks.pdf;

[P05] John C. Platt, “FastMap, MetricMap, and Landmark MDS are all Nystr¨om Algorithms”, 2005, downloadable at http://research.microsoft.com/en-us/um/people/ jplatt/nystrom2.pdf.

• *Assume that A is invertible, show that

det(K) = det(A) · det(K/A),

• *Assume that A is invertible, show that

rank(K) = rank(A) + rank(K/A).

• *Can you extend the identities in (c) and (d) to the case of noninvertible A? A good reference can be found at,

[Q81] Diane V. Quellette, “Schur Complements and Statistics”, Linear Algebra and Its Applications, 36:187-295, 1981. http://www.sciencedirect.com/science/ article/pii/0024379581902329