Kernel Information Embeddings
Kernel Information Embeddings (KIE) are probabilistic, non-linear latent variable models.
Shared Kernel Information Embeddings generalize KIE to multiple observation spaces.
Code
This Python module provides an implementation of
KIE and shared KIE using GPUs through Tijmen Tieleman's gnumpy package.
It further requires numpy and it can use
minimize.py for faster training.
This example-script demonstrates use of the model by instantiating and
applying it to this data-set.
References
2006 Memisevic, R.
Kernel Information Embeddings
International Conference on Machine Learning (ICML 2006).
[pdf]
2009 Sigal, L., Memisevic, R., Fleet, D.
Shared kernel information embeddings for discriminative inference
Computer Vision and Pattern Recognition (CVPR 2009).
[pdf]