Sentence examples for correlation among features from inspiring English sources

The feature grid diagram adopts the MDS embedding technique to visualize the correlation among features.

In general, association rules are used to describe dependence or correlation among features (or items).

When compared to early fusion, late fusion is easier to perform, but it cannot learn the correlation among features.

The fundamental reason for this increase of the chance of correlation among features is that in a high-dimensional feature vector there is the potential of many of its components being zero.

Feature aggregation instead of long-tailed concatenation should be considered to compactly represent each local region of the skull as it makes the features more distinctive with less correlation among features from the same class.

Besides that, it utilizes the correlation coefficient to characterize the correlation among features, and allow the tool users (i.e., the data managers) to directly observe the effects of the combination of features and sampling rates to better understand the utility and privacy information implied by the data to be shared.

A further refined system was then constructed by applying a maximum likelihood linear transform (MLLT) upon the LDA feature, which intended to reduce the correlation among feature dimensions so that the diagonal assumption of the Gaussians is satisfied.

Due to feature correlations among features, the m best separated features are not the best combined m features.

However, this is a slightly different issue as LCMNB was arguably concerned with reducing the chance of correlations among features by simply discarding contributions from absence of features to the estimation of (p omega _{j}|mathbf{x})).

In a word, a user can learn the followings through this module: (1) intuitively understanding the utility/privacy information implied by each feature, (2) observing the correlations among features, (3) figuring out the set of features, on which the sampling rate has the significant impact, (4) analyzing feature types and clustering according to the mutual information.

In Figure 3, a visual comparison is shown of these coefficients considered jointly, through simultaneous fits to all radiographic features; Figure S5 shows the same coefficients estimated by fitting to single radiographic features individually, i.e. not accounting for correlations among features.