Comments (2)
Based on a discussion with @steincaleb, I created a quick-and-dirty figure of classification accuracies that summarizes things quite nicely.
The classification error rates are obtained with 1000 reps. Standard errors are approximately 0.01 (difficult to show on the plot). The variables are selected using the blocking idea we discussed, so the results have a natural interpretation as truly having more information.
It's quite interesting to see that GRDA is hardly effected by the dimension of the data!
from paper-hdrda.
This is a better plot -- I've increased the line width so that it is easier to see.
from paper-hdrda.
Related Issues (20)
- Update abstract and intro from biology to generic high-dimensional discussion
- Revamp two computational complexity paragraphs HOT 2
- Investigate alpha_k = 1 - gamma HOT 3
- Rewrite results after sims are revamped
- Add timing comparison HOT 1
- Rewrite salespitch of HDRDA classifier in introduction
- Consider other classifiers HOT 1
- Compare classifiers on simulated data sets HOT 1
- Investigate metrics than error rate
- Apply CSDA LaTeX template HOT 1
- Upload final draft to arXiv HOT 7
- Write cover letter to CSDA
- Submit paper to CSDA HOT 2
- Apply edits from Mrs. Young
- Add link to GitHub repo in paper
- Add coauthor organizations to paper
- Add timing comparison HOT 8
- Update \oplus notation to 2x2 block-diagonal
- Finetune intro HOT 2
- Submit paper to JSCS HOT 2
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from paper-hdrda.