… correlation analysis (DCA), a feature level fusion technique that incorporates the class
associations into the correlation analysis of the feature sets. DCA performs an effective feature …

… analysis levels can be done at three levels namely document level, sentence level and Feature
level analysis… Aspect level was earlier called feature level (feature-based opinion mining …

… a discrepancy between the features of a technology-delivered … three of the most highly-accessed
features on the site, yet none … by conducting a feature-level analysis of an innovative …

… to learn abstract and invariant features directly from the data … (DBNs) and feature change
analysis to highlight changes. First, … Second, we map bitemporal change feature into a 2-D polar …

… fusion techniques to find the features which hold … feature selection stage, we analyze two
kinds of features named geometrically localized features (GLF) and surface curvature features (…

… automatically from user reviews, and thus to degrade product-level modeling to feature-level
modeling. To make it computationally feasible for time series analysis on daily resolution, …

… analysis. In unsupervised variance-based feature selection, we select the top 1000 features
with the highest variances from the training dataset, and then use them for survival analysis …

The tasks of feature-level opinion mining usually include the … to determine opinions at the
feature level: model based … of review elements, such as features, opinions, intensifiers, entity …

… In this section, we give some preliminaries to the analysis of feature dependencies, with
the purpose of helping readers build a clear view on the concept of features and how features …

… We compare the proposed feature level fusion technique with the serial feature fusion, the
parallel feature fusion, the CCA-based feature fusion, and the most recently published Joint …