m8ta
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{1557}  
The fact that sVD works at all, and pulls out some structure is interesting! Not nearly as good as word2vec.  
{1468} 
ref: 2013
tags: microscopy space bandwidth product imaging resolution UCSF
date: 06172019 14:45 gmt
revision:0
[head]


How much information does your microscope transmit?
 
{1371} 
ref: 0
tags: nanotube tracking extracellular space fluorescent
date: 02022017 22:13 gmt
revision:0
[head]


PMID27870840 Singlenanotube tracking reveals the nanoscale organization of the extracellular space in the live brain
 
{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. 