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sparse_methods.aux
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\relax
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\newlabel{eqn:reconstruction_error}{{3}{4}}
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\@writefile{loa}{\contentsline {algorithm}{\numberline {1}{\ignorespaces Algorithm for selecting most significant regions for predicting cognitive scores.}}{5}}
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\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Demonstration of ``Swiss roll'' data that demonstrates the utility of non-linear dimensionality reduction techniques. The data cannot be satisfactorily explained by a linear projection, but the non-linear manifold dimensionality reduction technique Locally Linear Embedding (LLE) successfully ``unwraps'' the curve and presents a reasonable two-dimensional decomposition of the data. Figure taken from \cite {roweis_nonlinear_2000}.}}{7}}
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\bibstyle{plain}
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