m8ta
you are not logged in, login. new entry 

{1416}  
Learning data manifolds with a Cutting Plane method
 
{1408}  
LDMNet: Low dimensional manifold regularized neural nets.
 
{796}  
An interesting field in ML is nonlinear dimensionality reduction  data may appear to be in a highdimensional space, but mostly lies along a nonlinear lowerdimensional subspace or manifold. (Linear subspaces are easily discovered with PCA or SVD(*)). Dimensionality reduction projects highdimensional data into a lowdimensional space with minimum information loss > maximal reconstruction accuracy; nonlinear dim reduction does this (surprise!) using nonlinear mappings. These techniques set out to find the manifold(s):
(*) SVD maps into 'concept space', an interesting interpretation as per Leskovec's lecture presentation. 