Man pages for mattdneal/GPLVM
Gaussian Process Latent Variable Models
| approx.hessian.vector.product | Title |
| fit.A | Fit function coefficients for target latent variable values. |
| fit.bcgplvm | Fit a back-constrained GPLVM model |
| fit.bcgplvm.sequential | Fit a backconstrained GPLVM model sequentially |
| fit.bcsgplvm | Fit a backconstrained structured GPLVM model |
| fit.gplvm | Fit an unconstrained GPLVM model |
| fit.lsa_bcsgplvm | Fit a large scale approximation back constrained structured... |
| generate.structured.dataset | Generate structured synthetic data |
| noisyComparison | Compare BCGPLVM to BCSGPLVM |
| predict.LSA_BCSGPLVM | Predict data for an LSA_BCSGPLVM model |
| replay.plots | Replay the plots from fitting an LSA_BCSGPLVM model |
| sample.from.model | Sample from a structured GPLVM model |
| select.bc.l.centile | Select a lengthscale for constrained optimization by... |
| select.bc.l.median | Select a lengthscale for constrained optimization by median |
| select.bc.l.minmax | Select a lengthscale for constrained optimization by min and... |
| sparse.gplvm | Sparse GPLVM |
| structured.gplvm.fixedC | Fit a structured GPLVM model with fixed structure covariance |
| structured.kernel.Matrix | Structured data kernel |
