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In practice, there exist three corresponding well-known models, including the Locality Preserving Projection (LPP), the Linear Discriminant Analysis (LDA), and the Maximum Margin Criterion (MMC).
Second, ASFDA seeks projections to minimize within-class temporal variation and maximize between-class temporal variation simultaneously based on maximum margin criterion.
Based on the objective function and the maximum margin criterion, a dimensionality reduction algorithm, the non-negative sparse semi-supervised maximum margin algorithm, is proposed.
In this study, a novel computationally efficient method is introduced by selecting the first few critical sensors based on a maximum margin criterion among different odor classes.
Instead, we propose to extend the learning process via a maximum margin criterion, in order to better encode discriminative information directly in features by optimizing them to be close to each other if from the same class and far from each other if from different classes.
maximum margin criterion.
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examples of the supervised methods are maximum margin criteria (MMC), maximum relevance minimum redundancy (mRMR) and linear discriminate analysis (LDA), (ii) the unsupervised technique, however, transform the existing features by rotating and projecting them onto a minimal number of axes without using the target labels.
Liu et al. proposed a neighborhood preserving ordinal regression method that tries to extract multiple projection directions from the original dataset according to maximum margin and manifold preserving criteria [25].
The maximum margin classifier, aka hard-margin support vector machine (SVM).
The poll has a maximum margin of sampling error of plus or minus three points.
orthogonal maximum margin projection subspace.
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