Research map / Probabilistic and scientific modeling / Variational and generative sampling
Markov Latent Feature Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Markov-chain feature correlations (iMLFM) instead of independent features (BPFA) | nonparametric latent feature model on HGDP-CEPH · HGDP-CEPH | predictive log-likelihood | Helped |
| Use Markov-chain feature correlations (iMLFM) instead of independent features (BPFA) | image denoising with SVI · Barbara, Lena, Peppers, Goldhill, σ=5 to 25 | PSNR | Mixed |
| Use iMLFM instead of K-SVD sparse coding | image denoising · Barbara and Peppers | PSNR | Mixed |
| Use iMLFM instead of K-SVD sparse coding | image denoising · Lena and Goldhill | PSNR | Hurt |
| Use iMLFM instead of K-SVD for sparsity | image denoising · Barbara σ=5 | average features per patch | Helped |
| Finite MLFM with K range 5-30 features vs nonparametric iMLFM (ablation) | HGDP-CEPH · HGDP-CEPH | predictive log-likelihood | No clear effect |
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