Research map / Learning theory and algorithms / Matrix and tensor methods
Partitioned Tensor Factorizations for Learning Mixed Membership Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Partitioned factorization with PTPQP (factorize O(p/k) sub-tensors of size O(k^3)) versus full tensor decomposition | mixed membership model parameter estimation · Crowdsourcing (5 datasets) | Incorrectly predicted labels (%) | Mixed |
| Replacing TPM/HALS/NOJD with PTPQP (nonnegative quadratic-programming factorization on partitions) | PTPQP · Crowdsourcing WEB | Incorrectly predicted labels (%): 14.44 vs 14.70 (TPM), 26.84 (HALS) | Helped |
| Using PTPQP instead of HALS (nonnegative factorization) on simulated GDLM data | PTPQP · Simulated GDLM, RMSE | RMSE | Helped |
| Using meld instead of PTPQP for small sample sizes and many components | simulated GDLM · Simulated GDLM, small n, large k | RMSE | Hurt |
| Increasing number of partitions in PTPQP | PTPQP · Simulated GDLM, p=1000, n=1000 | runtime | Helped |
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