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Lu et al. [27] proposed a multilabel-guided subspace (MLG) to better characterize and correlate the age and gender information of subjects, and in [28], they proposed an ordinary preserving manifold analysis approach to seek a low-dimensional discriminative subspace for age estimation tasks.
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Two ordinary preserving manifold analysis approaches proposed in [28], ordinary preserving linear discriminant analysis (OPLDA) and ordinary preserving margin Fisher analysis (OPMFA), were both evaluated in this study.
The methods tested against include a canonical correlation analysis approach and a manifold regularization approach, which is considered the baseline method.
Non-linear modal analysis approach based on invariant manifold method proposed earlier by Shaw and Pierre (Journal of Sound and Vibration164, 85 124) is utilized here to obtain the non-linear normal modes of a clamped clamped beam for large amplitude displacements.
Our manifold analysis reveals the lack of any detectable nanoporosity in water.
In this paper, we propose a novel supervised manifold learning approach, supervised locality discriminant manifold learning (SLDML), for head pose estimation.
The numerical simulations are based on a two-scalar flamelet manifold tabulation approach.
One of the approaches considered in [ 12] is the intrinsic low-dimensional manifold (ILDM) approach, which is an improved time-scale separation technique.
Besides, we also believe that, by implementing the manifold fitting approach proposed by Zhang et al. [ 31], the performance of AMPs predictor can be enhanced.
This paper presents a method to design a reduced order observer using an invariant manifold approach.
In this paper, a damage index is designed by performing manifold distance analysis on vibration signal.
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