Research map / Learning theory and algorithms / Matrix and tensor methods
Mixed Linear Regression with Multiple Components
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use LOSCO alternating minimization with random initialization instead of EM with random initialization | LOSCO-ALT · MLR d=1k, N=60k | convergence to optimum | Helped |
| use tensor initialization instead of random for EM | EM · MLR d=100, N=6k | convergence | Helped |
| increase number of samples N | LOSCO · MLR noisy case, d=100 | solution error | Helped |
| increase noise standard deviation | LOSCO · MLR noisy case | solution error | Hurt |
| increase dimension d | LOSCO · MLR noiseless exact recovery | samples needed | Hurt |
| use PSC instead of SSC as N/K grows from 200 to 1000 | PSC · Subspace clustering time | time (sec): 1738.82 -> 0.76 | Helped |
| use PSC instead of NSN+GSR at N/K=1000 | PSC · Subspace clustering time | time (sec): 83.96 -> 0.76 | Helped |
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