Title
Coarse Graining of Data via Inhomogeneous Diffusion Condensation.
Abstract
Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here, we consider multiple levels of abstraction via a multiresolution geometry of data points at different granularities. To construct this geometry we define a time-inhomogemeous diffusion process that effectively condenses data points together to uncover nested groupings at larger and larger granularities. This inhomogeneous process creates a deep cascade of intrinsic low pass filters on the data affinity graph that are applied in sequence to gradually eliminate local variability while adjusting the learned data geometry to increasingly coarser resolutions. We provide visualizations to exhibit our method as a “continuously-hierarchical” clustering with directions of eliminated variation highlighted at each step. The utility of our algorithm is demonstrated via neuronal data condensation, where the constructed multiresolution data geometry uncovers the organization, grouping, and connectivity between neurons.
Year
DOI
Venue
2019
10.1109/BigData47090.2019.9006013
BigData
Keywords
Field
DocType
diffusion,graph signal processing,hierarchical clustering,manifold learning
Hierarchical clustering,Data point,Data mining,Diffusion process,Computer science,Algorithm,Cascade,Granularity,Nonlinear dimensionality reduction,Cluster analysis,Big data
Conference
Volume
Citations 
PageRank 
2019
0
0.34
References 
Authors
0
10