Spike and Slab Gaussian Process Latent Variable Models

@article{Dai2015SpikeAS,
  title={Spike and Slab Gaussian Process Latent Variable Models},
  author={Zhenwen Dai and James Hensman and Neil D. Lawrence},
  journal={CoRR},
  year={2015},
  volume={abs/1505.02434}
}
The Gaussian process latent variable model (GPLVM) is a popular approach to non-linear probabilistic dimensionality reduction. One design choice for the model is the number of latent variables. We present a spike and slab prior for the GP-LVM and propose an efficient variational inference procedure that gives a lower bound of the log marginal likelihood. The new model provides a more principled approach for selecting latent dimensions than the standard way of thresholding the length-scale… CONTINUE READING