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You have a data matrix X € Rnxd where each row corresponds to a student and each column corresponds to their grade on an assignment. The final column is their cumulative grade. All grades are numerical values. Give an upper bound on the rank of this matrix. Do you think it is well approximated by an even lower rank matrix? Why or why not?

You have a data matrix X € Rnxd where each row corresponds to a student and each column corresponds to their grade on an assignment. The final column is their cumulative grade. All grades are numerical values. Give an upper bound on the rank of this matrix. Do you think it is well approximated by an even lower rank matrix? Why or why not?

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
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1. You have a data matrix X = Rnxd where each row corresponds to a student and each column corresponds to their grade on an assignment. The final column is their cumulative grade. All grades are numerical values. Give an upper bound on the rank of this matrix. Do you think it is well approximated by an even lower rank matrix? Why or why not?
Transcribed Image Text:1. You have a data matrix X = Rnxd where each row corresponds to a student and each column corresponds to their grade on an assignment. The final column is their cumulative grade. All grades are numerical values. Give an upper bound on the rank of this matrix. Do you think it is well approximated by an even lower rank matrix? Why or why not?
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