Venables and Ripley ( 1994 ) describe an efficient algorithm ( of linear complexity in the number of training sentences ) for computing the LDA transform matrix , which entails computing the withinand between-covariance matrices of the classes , and using Singular Value Decomposition ( SVD ) to compute the eigenvectors of the new space .
modern applied statistics with splus
William N Venables Brian D Ripley
In an attempt to further boost performance , we employed Linear Discriminant Analysis ( LDA ) to find a linear projection of the four-dimensional vectors that maximizes the separation of the Gaussians ( corresponding to the HMM states ) . Venables and Ripley ( 1994 ) describe an efficient algorithm ( of linear complexity in the number of training sentences ) for computing the LDA transform matrix , which entails computing the withinand between-covariance matrices of the classes , and using Singular Value Decomposition ( SVD ) to compute the eigenvectors of the new space . Each sentence/vector is then multiplied by this matrix , and new HMM models are re-computed from the projected data .
@@CITATION describe an efficient algorithm ( of linear complexity in the number of training sentences ) for computing the LDA transform matrix , which entails computing the withinand between-covariance matrices of the classes , and using Singular Value Decomposition ( SVD ) to compute the eigenvectors of the new space .