1.

M. R. Anderberg, *Cluster Analysis for Applications*, Academic Press, New York and London, 1973.

2.

M. W. Berry, S. T. Dumais, and G. W. O'Brien, *Using linear algebra for intelligent information retrieval*, SIAM Review, 37 (1995), pp. 573-595.

3.

M. W. Berry, Z. Drmac, and E. R. Jessup, *Matrices, vector spaces, and information retrieval*, SIAM Review, 41 (1999), pp. 335-362.

4.

Å. Björck, *Numerical Methods for Least Squares Problems*, SIAM, Philadelphia, PA, 1996.

5.

J. R. Colon and S. J. Colon, *Optimal use of an information retrieval system*, J. Amer, Soc. Information Science, 47:6 (1996), pp. 449-457.

6.

M. T. Chu, R. E. Funderlic, and G. H. Golub, *A rank-reduction formula and its applications to matrix factorizations*, SIAM Review, 37 (1995), pp. 512-530.

7.

R. E. Cline and R. E. Funderlic, *The rank of a difference of matrices and associated generalized inverses*, Linear Algebra Appl., 24 (1979), pp. 185-215.

8.

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, *Indexing by latent semantic analysis*, J. Soc. Information Science, 41 (1990), pp. 391-407.

9.

I. S. Dhillon and D. S. Modha, *Concept Decompositions for large sparse text data using clustering*, Machine Learning 421 (2001), pp. 143-175.

10.

S. T. Dumais, *Improving the retrieval of information from external sources*, Behav-ior Research Methods, Instruments, & Computers, 23 (1991), pp. 229-236.

11.

C. Eckart and G. Young, *The approximation of one matrix by another lower rank*, Psychometrika, 1 (1936), pp. 211-218.

12.

W. B. Frakes and R. Baeza-Yates, *Information Retrieval: Data Structures and Algorithms*, Prentice-Hall, Englewood Cliffs, NJ, 1992.

13.

M. D. Gordon, *Using latent semantic indexing for literature based discovery*, J. Amer. Soc. Information Science, 498 (1998), pp. 674-685.

14.

G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd ed., Johns Hopkins University Press, Baltimore, 1996.

15.

E. Gose, R. Johnsonbaugh and S. Jost, *Pattern Recognition and Image Analysis*, Prentice-Hall, Upper Saddle River, NJ, 1996.

16.

L. Guttman, *A necessary and sufficient formula for matrix factoring*, Psychometrika, 22 (1957), pp. 79-81.

17.

S. Harter, *Psychological relevance and information science*, J. Amer. Soc. Information Science, 439 (1992), pp. 602-615.

18.

H. S. Heaps, *Information Retrieval, Computational and Theoretical Aspects*, Academic Press, New York, 1978.

19.

L. Hubert, J. Meulman, and W. Heiser, *Two purposes for matrix factorization: A historical appraisal*, SIAM Review, 421 (2000), pp. 68-82.

20.

A. K. Jain, and R. C. Dubes, *Algorithms for Clustering Data*, Prentice-Hall, 1988.

21.

M. Jeon, *Centroid-Based Dimension Reduction Methods for Classification of High Dimensional Text Data*, Ph.D. Dissertation, University of Minnesota, June 2001.

22.

Y. Jung, H. Park, and D. Du, *An Effective term-weighting scheme for information retrieval*, Tech. Report TR00-008, Department of Computer Science and Engineering, University of Minnesota, MN, USA, 2000.

23.

Y. Jung, H. Park, and D. Du, *A Balanced term-weighting scheme for improved document comparison and classification*, preprint, 2001.

24.

G. Kowalski, *Information Retrieval System: Theory and Implementation*, Kluwer Academic Publishers, Dordrect, 1997.

25.

H. Kim, P. Howland, and H. Park, *Text categorization using support vector machines with dimension reduction*, Tech. Report TR 03-014, Department of Computer Sciece and Engineering, University of Minnesota, MN, USA, 2003.

26.

T. G. Kolda, *Limited-memory matrix methods with applications*, Ph.D. dissertation, Applied Mathematics, University of Maryland, 1997.

27.

T. G. Kolda and D. P. O'Leary, *A semi-discrete matrix decomposition for latent semantic indexing in information retrieval*, ACM Trans. Information Systems, 16 (1996), pp. 322-346.

28.

R. Krovetz and W. B. Croft, *Lexical ambiguity and information retrieval*. ACM Trans. Information Systems, 102 (1992), pp. 115-241.

29.

M. Nadler and E. P. Smith, *Pattern Recognition Engineering*, Wiley, 1993.

30.

R. T. Ng and J. Han, *Efficient and effective clustering methods for spatial data mining*, in *Proceedings of the 20th International Conference on Very Large Databases*, 1994, pp. 144-155.

31.

A. M. Pejtersen, *Semantic information retrieval*, Comm. ACM, 414 (1998), pp. 90-92.

32.

J. B. Rosen, H. Park, and J. Glick, *Total least norm formulation and solution for structured problems*, SIAM J. Matrix Anal. Appl., 17 (1996), pp. 110-128.

33.

G. Salton, *The SMART Retrieval System*, Prentice-Hall, Englewood Cliffs, NJ, 1971.

34.

G. Salton, and M. J. McGill, *Introduction to Modern Information Retrieval*, Mc-Graw Hill, 1983.

35.

S. Theodoridis and K. Koutroumbas, *Pattern Recognition*, Academic Press, 1999

36.

D. Zhang, R. Ramakrishan, and M. Livny, *An efficient data clustering method for very large databases*, in *Proceedings of the ACM SIGMOD Conference on Management of Data*, Montreal, Canada, June 1996.