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Low-level feature encoding combined with Spatial Pyramid Matching (SPM) is widely adopted in the image classification system nowadays to extract features, which are usually high-dimensional. This not only makes the classification problem computationally prohibitive, but also raises other issues, such as the “curse of dimensionality”. In this paper we present supervised dimensionality reduction (DR)...
We propose a new sparsity-promoting objective function to be used in sparse signal recovery. Specifically, the objective is an entropy function 𝑙1 defined on the sparse signal x. Compared to the conventional 𝑙1, it is a nonconvex function and the optimization problem can be solved based on the fast iterative shrinkage thresholding algorithm (FISTA). Experiments on 1-dimensional sparse signal recovery...
The low-rank matrix recovery problem consists of reconstructing an unknown low-rank matrix from a few linear measurements, possibly corrupted by noise. One of the most popular method in low-rank matrix recovery is based on nuclear-norm minimization, which seeks to simultaneously estimate the most significant singular values of the target low-rank matrix by adding a penalizing term on its nuclear norm...
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