-
Structure: add intro
-
LSH: add rho
-
emphasize bilinearity, generates H from X
-
vectorspace => vector space
-
m and t in 4.1 Normal distribution in order to approximate the covariance of the Phi(X), which can be turned into a normal gaussian
- Introduction - briefly LSH algorithm + kernel methods / how to kernelize is goal
- Kernel theory + why kernelize
- LSH ball carving method
- Need Gaussian => getting a normal distribution in kernel
- Total KLSH algorithm
- Extending KLSH
Daniel:
- unusual similarity measures
- ball carving between KLSH intro & normal distribution
- further directions/data-dependent
Geelon:
- expand the practical part of kernel
- normal distribution + covariance estimation