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2011
IEEE

Partial generalized correlation for hyperspectral data

8 years 10 months ago
Partial generalized correlation for hyperspectral data
Abstract—A variational approach is proposed for the unsupervised assessment of attribute variability of high-dimensional data given a differentiable similarity measure. The key question addressed is how much each data attribute contributes to an optimum transformation of vectors for reaching maximum similarity. This question is formalized and solved in a mathematically rigorous optimization framework for each data pair of interest. Trivially, for the Euclidean metric minimization to zero distance induces highest vector similarity, but in case of the linear Pearson correlation measure the highest similarity of one is desired. During optimization the not necessarily symmetric trajectories between two vectors are recorded and analyzed in terms of attribute changes and line integral. The proposed formalism allows to assess partial covariance and correlation characteristics of data attributes for vectors being compared by any differentiable similarity measure. Its potential for generating...
Marc Strickert, Bjorn Labitzke, Volker Blanz
Added 25 Aug 2011
Updated 25 Aug 2011
Type Journal
Year 2011
Where CIDM
Authors Marc Strickert, Bjorn Labitzke, Volker Blanz
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