From dc02e7af89e6f59bb9ef36d359e7cabd668f1d4f Mon Sep 17 00:00:00 2001 From: Kunal Arora Date: Fri, 30 Jan 2015 15:05:48 +0530 Subject: [PATCH] each eigenvectors-->each eigenvector --- src/shogun/converter/Isomap.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/shogun/converter/Isomap.h b/src/shogun/converter/Isomap.h index 194f8788ddc..179cd11e273 100755 --- a/src/shogun/converter/Isomap.h +++ b/src/shogun/converter/Isomap.h @@ -40,7 +40,7 @@ class CDistance; * - Compute squared distances matrix \f$D\f$ such as \f$D\{i,j\} = d^2(x_i,x_j)\f$. * - Relax distances with shortest(so-called geodesic) distances on the sparse neighbourhood graph (e.g. with sparse Dijkstra algorithm). * - Center the matrix \f$D\f$ with subtracting row mean, column mean and adding to the grand mean. Multiply \f$D\f$ element-wise with \f$-0.5\f$. - * - Compute embedding with the \f$t\f$ eigenvectors that correspond to the largest eigenvalues of the matrix \f$D\f$; normalize these vectors dividing each eigenvectors with square root of its corresponding eigenvalue. Form the final embedding with eigenvectors as rows and projected feature vectors as columns. + * - Compute embedding with the \f$t\f$ eigenvectors that correspond to the largest eigenvalues of the matrix \f$D\f$; normalize these vectors dividing each eigenvector with square root of its corresponding eigenvalue. Form the final embedding with eigenvectors as rows and projected feature vectors as columns. * It is possible to apply preprocessor to specified distance using * apply_to_distance.