Tipping and Bishop developed a probabilistic version of this technique, which works by marginalizing  over a continuous latent space in which for each data point there is a corresponding latent variable. A  canonical way of carrying out this technique is to compute the singular value decomposition of the data  matrix, the columns of which yield the vectors sought by this technique. The application of this technique  to time series is cal ed the empirical orthogonal function method, and this technique involves the  diagonalization of the covariance matrix to find the n  eigenvectors corresponding to the n largest eigenvalues. For 10 points, identify this dimensionality reduction technique in which data is projected  
Tipping and Bishop developed a probabilistic version of this technique, which works by marginalizing  over a continuous latent space in which for each data point there is a corresponding latent variable. A  canonical way of carrying out this technique is to compute the singular value decomposition of the data  matrix, the columns of which yield the vectors sought by this technique. The application of this technique  to time series is cal ed the empirical orthogonal function method, and this technique involves the  diagonalization of the covariance matrix to find the n  eigenvectors corresponding to the n largest eigenvalues. For 10 points, identify this dimensionality reduction technique in which data is projected  onto a lower-dimensional linear space such that the variance of the projected data is maximized.
Tipping and Bishop developed a probabilistic version of this technique, which works by marginalizing  over a continuous latent space in which for each data point there is a corresponding latent variable.
principal component analysis or PCA