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These methods ignore the relation between different feature types.
Instead of mutual information, generalized correlation is adopted in minimal redundancy maximal relevance to measure the relation between different feature types.
Most existing feature selection methods focus on predictability (e.g., prediction accuracy) without considering the correlation between different feature types, weakening the interpretability of selected results.
Given a set of distinct feature types, the relation between different feature types is measured by minimal redundancy maximal relevance based on generalized correlation.
For example, Wang et al. [22] and Somol et al. [23] proposed to select the most discriminative feature types based on the relationships between different feature types, both methods are sparse feature selections rather than filter methods.
where ρ represents generalized correlation coefficient between different feature types, (bar {G}^{(i)}) the i-th selected feature type, and G j) denotes a certain feature type from the candidate feature set, (F^G-F^{G^{prime }}_{v-1}).
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However, when the far more challenging cross-donor validation is attempted, strong differences become apparent between the predictive powers of different feature types.
The face detection model comprises nine stages with different feature types and feature counts.
To discover the contribution of each feature type, we further investigate the distribution of different feature types in the final optimal feature subset and 5(b)).
There was no significant difference between different pathway categories or between different feature vectors.
Within the ventral stream, biased competition operates between different features and different objects.
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CEO of Professional Science Editing for Scientists @ prosciediting.com