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Recently sparse coding has received expressions of interest in the field of pattern recognition. Most existing methods take the data-as-vector formulation, and deal with images (the second order tensor) or volumes (the third order tensor) by vectorization. However, such kind of vectorization will lose the original structure of the data and reduce the reliability of post processing, leading a poor...
Recent years have witnessed a growing interest in the sparse representation problem. Prior work demonstrated that adaptive dictionary learning techniques can greatly improve the performance of sparse representation approaches. Existing techniques mainly focus on the reconstructive accuracies and the discriminative power of the learned dictionary, whereas the mutual incoherence between any two basis...
A quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA). It is inspired by the fact that images are transformed into sparse representations by the primary visual cortex which is the most important part of the human visual system (HVS). The proposed method is based on sparse features that are acquired from a set of feature detectors called...
We propose a novel joint dynamic sparsity regularization for joint learning of multiple tasks (i.e., multiple observations of the same physical event by a set of homogeneous or heterogenous sensors). The proposed method not only combines the strength of different tasks but also has the flexibility of selecting a set of different atoms for each task, with a class-wise constraint, which is more flexible...
We present a sparse representation-based method for detecting adventitious lung sounds in low-quality auscultation signals. Since the noise cannot be represented sparsely by any bases, we can extract clear breath sounds and adventitious sounds from noisy electronic auscultation signals via the sparse representation. Using these clear sound components, we measure the level of abnormality, and robustly...
Due to the fact that many objects in the real world can be naturally represented as tensors, tensor subspace analysis has become a hot research area in pattern recognition and computer vision. However, existing tensor subspace analysis methods cannot provide an intuitionistic nor semantic interpretation for the projection matrices. In this paper, we propose Sparse Tensor Principal Component Analysis...
Microarrays are massively parallel biosensors that can simultaneously detect and quantify a large number of different genomic particles. A DNA microarray is a nucleic acid-based microarray that contains probe spots testing a multitude of targets in one experiment. Ideas from compressive sensing have been utilized in different ways in the analysis of DNA microarrays. One of the proposed methods is...
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