| C02-1081 | interpretable components by means of a | principal component analysis | . Classes were established from |
| D14-1216 | events . FDA is closely related to | principal component analysis | ( PCA ) , where a linear combination |
| C88-2135 | surface characteristics , the | principal component analysis | ( PCA ) extracts factors of variance |
| D14-1193 | training instances . Here we employ | Principal component analysis | ( PCA ) . This is because PCA |
| D10-1076 | initialization scheme , we also used a | principal component analysis | to represent the induced word |
| D14-1101 | learned representations , we applied | principal components analysis | ( PCA ) to the hidden activations |
| D13-1058 | forms a preprocessing step in | principal component analysis | and Fisher linear discriminant |
| C04-1190 | Component Analysis The Kernel | Principal Component Analysis | technique , or KPCA , is a method |
| D09-1065 | Pulverm ¨ uller , 2005 ) . A | principal components analysis | can also be performed on the |
| C04-1190 | WSD applications . 3.1 Kernel | Principal Component Analysis | The Kernel Principal Component |
| C88-2135 | To find the proper weighting , | principal component analysis | ( PCA ) was appliedto these characteristics |
| C02-1081 | are computed as input for the | principal component analysis | ( PCA ) . The PCA was done with |
| D11-1005 | for each tested language using | principal component analysis | and plotted the result in Figure |
| D11-1043 | statistical techniques such as | Principal Component Analysis | may play a constructive role |
| D10-1025 | low-rank Gaussian . 2.2 Oriented | Principal Component Analysis | The limitations of CL-LSI can |
| C04-1190 | that exploits a nonlinear Kernel | Principal Component Analysis | ( KPCA ) technique to make predictions |
| C94-1084 | the re - sults . We carried out | Principal Component Analysis | ( PCA ) with a set of fifteen |
| C02-1087 | field of linear algebra , PCA ( | Principal Component Analysis | ) , SVD ( Singular Value Decomposition |
| D14-1190 | decomposes B using Probabilistic | Principal Component Analysis | : B = UAUT + diag ( α ) |
| C04-1190 | that also makes use of Kernel | Principal Component Analysis | ( KPCA ) , proposed by ( Sch |
