Diffusion bases methods for segmentation and clustering

Details Diffusion bases methods for segmentation and clustering Diffusion bases methods for segmentation and clustering

2011-06-14

2011-06-14

Akhavannik, Hadi (Department: 2624)

Image analysis

Pattern recognition

Classification

C382S274000, C382S166000, C382S180000, C382S284000, C702S019000

Reexamination Certificate

active

07961957

ABSTRACT:
Methods for dimensionality reduction of large data volumes, in particular hyper-spectral data cubes, include providing a dataset Γ of data points given as vectors, building a weighted graph G on Γ with a weight function wε, wherein wεcorresponds to a local coordinate-wise similarity between the coordinates in Γ; obtaining eigenvectors of a matrix derived from graph G and weight function wε, and projecting the data points in Γ onto the eigenvectors to obtain a set of projection values ΓBfor each data point, whereby ΓBrepresents coordinates in a reduced space. In one embodiment, the matrix is constructed through the dividing each element of wεby a square sum of its row multiplied by a square sum of its column. In another embodiment the matrix is constructed through a random walk on graph G via a Markov transition matrix P, which is derived from wε. The reduced space coordinates are advantageously used to rapidly and efficiently perform segmentation and clustering.

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