The motivation behind MKL is to combine or fuse multiple kernels or features rather than using a single feature representation to make prediction, with expectation that such combination leads to potential gain in performance.
Then, by successfully determining the parameters γ l (l=0,1,⋯,L) in the multiple kernel function, the features can be mapped into the optimal feature space, enabling accurate ordinal regression.
In this study, we designed a feature fusion-based localized multiple kernel learning algorithm using the SPM feature to overcome the mentioned difficulties.
To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization.
This paper proposes a feature fusion based multiple kernel learning (MKL) model for image classification.
To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold.
By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively.
Anderson et al. [14] combine both static and dynamic features in a multiple kernel learning framework to find a weighted combination of the data sources that produced an effective classification.
Figure 8 shows the receiver operating characteristic (ROC) curves that compare verification performance of the features obtained from the Eigenfaces [13], scale-invariant feature transform (SIFT) [75], LBP [76], multiple kernel learning (MKL) [77], and the DGHMs.
Multiple kernel learning (MKL) methods are widely adopted to learn the feature weights and to fuse features on score-level.
