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Image inpainting based on sparse representation using self-similar joint sparse coding
In order to improve the sparse coding ability of over-complete dictionary and take advantage of the similarity between damaged pixels and their...
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Semi-supervised sparse representation collaborative clustering of incomplete data
Sparse subspace clustering (SSC) focuses on revealing the structure and distribution of high dimensional data from an algebraic perspective. It is a...
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Multi-view hyperspectral image classification via weighted sparse representation
Hyperspectral image classification aims to classify pixels in hyperspectral image into different land-cover classes. Considering multi-view data can...
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A unified kernel sparse representation framework for supervised learning problems
For supervised learning problems, a unified kernel sparse representation framework is proposed. It is applicable to almost all supervised learners in...
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Multi-view subspace clustering for learning joint representation via low-rank sparse representation
Multi-view data are generally collected from distinct sources or domains characterized by consistent and specific properties. However, most existing...
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BSPADMM: block splitting proximal ADMM for sparse representation with strong scalability
Sparse representation (SR) is a fundamental component of linear representation techniques and plays a crucial role in signal processing, machine...
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Overcomplete-to-sparse representation learning for few-shot class-incremental learning
Few-shot class-incremental learning (FSCIL) aims to continually learn new semantics given a few training samples of new classes. As training examples...
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Improved image representation and sparse representation for face recognition
Sparse representation is of great significance to the research of face recognition. Due to factors such as illumination, angle, and facial features,...
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CCIM-SLR: Incomplete multiview co-clustering by sparse low-rank representation
Clustering incomplete multiview data in real-world applications has become a topic of recent interest. However, producing clustering results from...
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Recent advances via convolutional sparse representation model for pixel-level image fusion
Image fusion aims to integrate complementary information from different source images into the final output image. This plays a significant role in...
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A new multi-focus image fusion quality assessment method with convolutional sparse representation
Assessing image fusion quality purposefully is a challenging task due to the diversities of fused features. In this work, a specific multi-focus...
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Moving object detection in gigapixel-level videos using manifold sparse representation
Moving object detection (MOD) is one of the most important and challenging tasks in analyzing videos. Recently, emerging gigapixel-level videos have...
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Spectral pursuit for simultaneous sparse representation with accuracy guarantees
The goal of simultaneous sparse representation is to capture as much information as possible from a target matrix by a linear combination of several...
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Dictionary reduction in sparse representation-based classification of motor imagery EEG signals
Recently, sparse representation-based classification has turned into a successful technique for motor imagery electroencephalogram signal analysis....
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Sparse Frame-Polynomial Coupling Representation
Frame representation is widely used in data acquisition, processing, and transmission systems. Due to abrupt discontinuity, the corresponding frame...
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Reconstruction of signals with sparse representation in optimally dilated Hermite basis
Compressive sensing (CS) provides a set of powerful techniques for the reconstruction of signals with a sparse representation in some particular...
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Haptic Recognition of Texture Surfaces Using Semi-Supervised Feature Learning Based on Sparse Representation
Haptic cognitive models are used to map the physical stimuli of texture surfaces to subjective haptic cognition, providing robotic systems with...
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Sparse Representation Frameworks for Acoustic Scene Classification
This work addresses the task of acoustic scene classification (ASC) by using sparse representation frameworks, motivated by the inherent sparseness...
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A Biologically-Inspired Sparse Self-Representation Approach for Projected Fuzzy Double C-Means Clustering
Data redundancy is frequently encountered in biologically data. Locality preserving projection (LPP) is a dimensionality reduction approach to...
