| Background and Related Works | empirical accuracy on prior knowledge and maximum entropy by finding the top L eigenvectors of an extended covariance matrix2 via PCA or SVD . |
| Background and Related Works | However, despite of the potential problems of numerical stability, SVD requires massive computational space and 0(M3) computational time where M is feature dimension, which limits its usage for high-dimensional data (Trefethen et al., 1997). |
| The direction is determined by concatenating w L times. | This is mainly because SSH requires SVD to find the optimal hashing functions which is computational expensive. |
| Experiments | Latent Semantic Analysis (LSA; Deerwester et al., 1990) We apply truncated SVD to a tf.idf weighted, cosine normalized count matrix, which |
| Related work | Latent Semantic Analysis (LSA), perhaps the best known VSM, explicitly learns semantic word vectors by applying singular value decomposition ( SVD ) to factor a term—document co-occurrence matrix. |
| Related work | It is typical to weight and normalize the matrix values prior to SVD . |