Yousef Saad Department Of Computer Science And Engineering

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Unsupervised learning: Clustering Problem: partition a given set into subsets such that items of the same subset are most similar and those of two different subsets most dissimilar. Superconductors Multi−ferroics Superhard Catalytic Photovoltaic Ferromagnetic Thermo−electric ➤ Basic technique: K-means algorithm [slow but effective.] ➤ Example of application : cluster bloggers by ‘social groups’ (anarchists, ecologists, sports-fans, liberals-conservative, ... ) CSE Colloquium 11/09/09 p. 28
Sparse Matrices viewpoint * Joint work with J. Chen ➤ Communities modeled by an ‘affinity’ graph [e.g., ’user A sends frequent e-mails to user B’] ➤ Adjacency Graph represented by a sparse matrix ➤ Goal: find ordering so blocks are as dense as possible: ➤ Use ‘blocking’ techniques for sparse matrices CSE Colloquium 11/09/09 p. 29
Page 1 and 2: From data-mining to nanotechnology
Page 3 and 4: ➤ Then the stock market tanked ..
Page 5 and 6: Electronic structure ➤ Quantum me
Page 7 and 8: ➤ Hamiltonian H is of the form :
Page 9 and 10: The underlying physical laws necess
Page 11 and 12: Kohn-Sham equations → nonlinear e
Page 13 and 14: The physical domain CSE Colloquium
Page 15 and 16: CHEBYSHEV FILTERING
Page 17 and 18: Chebyshev Subspace iteration - expe
Page 19 and 20: NUMERICAL LINEAR ALGEBRA IN DATA MI
Page 21 and 22: Major tool of Data Mining: Dimensio
Page 23 and 24: Linear Dimensionality Reduction Giv
Page 25 and 26: 2-D ‘reductions’: 15 10 5 0 −
Page 27: 2-D ’reductions’: 6 4 2 0 −2
Page 31 and 32: Supervised learning: classification
Page 33 and 34: A harder case: 4 3 2 1 0 −1 −2
Page 35 and 36: Fisher’s Linear Discriminant Anal
Page 37 and 38: ➤ Project set on a one-dimensiona
Page 39 and 40: Trace ratio problem * Joint work wi
Page 41 and 42: Background: The Lanczos procedure A
Page 43 and 44: Information Retrieval: Vector Space
Page 45 and 46: Common approach: Use the SVD ➤ Ne
Page 47 and 48: Polynomial filters - examples Appro
Page 49 and 50: IR: Use of the Lanczos algorithm (1
Page 51 and 52: ➤ Theory well understood: works w
Page 53 and 54: Average query times average query t
Page 55 and 56: In summary: ➤ Results comparable
Page 57 and 58: Difficulty Positions, Expressions,
Page 59 and 60: Tests: Face Recognition Tests with
Page 61 and 62: GRAPH-BASED METHODS
Page 63 and 64: Example: The Laplacean eigenmaps La
Page 65 and 66: A unified view * Joint work with Ef
Page 67 and 68: Graph-based methods in a supervised
Page 69 and 70: Repulsion Laplaceans * Joint work w
Page 71 and 72: Materials informatics at work. Illu
Page 73 and 74: Conclusion ➤ Many, many, interest

Yousef Saad Department Of Computer Science And Engineering

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