Latent Continuous Space

A Latent Continuous Space is a latent space that is a continuous space/vector space.

• Example(s):
  • Latent Word Distribution Space.
  • …
• Counter-Example(s):
  • Continuous Dataset.
• See: Latent Discrete Space, Latent Factor Analysis.

---

---

References

2015

• http://wikipedia.org/wiki/Nonlinear_dimensionality_reduction#Gaussian_process_latent_variable_models
  • QUOTE: The model is defined probabilistically and the latent variables are then marginalized and parameters are obtained by maximizing the likelihood. Like kernel PCA they use a kernel function to form a non linear mapping (in the form of a Gaussian process). However in the GPLVM the mapping is from the embedded (latent) space to the data space (like density networks and GTM) whereas in kernel PCA it is in the opposite direction.

2004

• (Lawrence, 2004) ⇒ Neil D. Lawrence. (2004). “Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data.” In: Advances in Neural Information Processing Systems, 16(3).
  • QUOTE: … of the data to be one — the images are produced in a smooth way over time which can be thought of as a piece of string embedded in a … Bottom'. Examples from the data-set which are closest to the corresponding fantasy images in latent space. … Stochastic neighbor embedding. …

---