Week 2 Agenda

Lecture Session

Visualizing Matrix Operations
SVD and PCA Review
If we have time, I will start with:
Overview of Document Indexing and Ranking Algorithms
First-Breadth and Deep-First Web Crawlers
The Terrier Desktop Searches Platform (Java)

Lab Session

Complete Lab 1. Please add the following instructions to the lab.

In Part 3, section 3.1.3, add the following task:

Compute the sum of the eigenvalues of ATA and the trace of this matrix. Do the same for AkTAk. Compare results and draw some conclusions. What important property is confirmed?

In Part 3, section 3.1.4, add the following task:

Finally, column-normalize VkT and construct a similarity matrix from it. Extract scalar clusters from it. Compare with the clusters extracted from AkTAk. Explain your observations.

In Part 4, section 4.1.1, add the following task:

Using EXCEL, reproduce the PCA example given by Smith in reference 4. Show all calculations.

Teaser: Consider the following lecture material list. Which trick is being used to reduce link juice (importance)? How would you add link juice?

Lecture Material

1. Using latent semantic analysis to improve access to textual information; Dumais, S. T., Furnas, G. W., Landauer, T. K., Deerwester, S., & Harshman, R. (1988). Proceedings of the Conference on Human Factors in Computing Systems, CHI. 281-286,

2. Indexing by Latent Semantic Analysis; Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990).

3. Association and Scalar Clusters Tutorial; Garcia, E. (2008).

4. A tutorial on Principal Components Analysis; Smith Lindsay (2002).

5. A tutorial on Principal Component Analysis; Jon Shlens (2003).

6. Do Your Worst to Make the Best: Paradoxical Effects in PageRank Incremental Computations
Boldi, P., S. Massimo, V. Sebastiano Vigna (2007)

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