In this study, we again adopt the Lambertian model, but solve for the normal vectors via a sparse representation framework that estimates both the normals and non-Lambertian errors at the same time.
These support points are generated in a sparse grid framework based on the hierarchial tensor products of the sub-grids and the appropriate statistical properties.
In particular, the paper will focus on a recently proposed face recognition algorithm, where a sparse representation framework has been used to recover human identities from facial images that may be affected by illumination, occlusion, and facial disguise.
The methodology utilizes a statistical learning algorithm called relevance vector machines (RVM), which is a sparse Bayesian framework that can be used for obtaining solutions to regression and classification tasks.
To infer cell-type diversity from partial or incomplete transcription factor expression data, we devised a sparse Bayesian framework that is able to handle estimation uncertainty and can incorporate diverse cellular characteristics to optimize experimental design.
Argyriou et al. [19] recently also adopt a sparse representation framework to decide the weights for finding the best illuminants to use, again with the lighting directions unknown.
Recently, the source localization problem has been converted into a sparse recovery framework, because the number of actual sources of interest is generally much smaller than the number of potential source locations in the region to be observed.
In designed experiments such as [8], BFRM provides a sparse ANOVA framework for studying changes associated with environmental stresses.
Instead of this, we employ a sparse regression framework that has recently emerged as a powerful tool for detecting associations in a high-dimensional space.
To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework.
