ℓ1-K-SVD: A robust dictionary learning algorithm with simultaneous update

Mukherjee, Subhadip; Basu, Rupam; Seelamantula, Chandra Sekhar

doi:10.1016/j.sigpro.2015.12.008

Cited by 35 publications

(18 citation statements)

References 37 publications

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“…This property is achieved by our multidimensional representation, as well as the orthogonality of the dictionaries. Finally, compared to the widely used 1D dictionary training algorithms [162,165,172,175,192], the AMDE has orders of magnitude smaller memory footprint.…”

Section: Summary and Future Workmentioning

confidence: 99%

Sparse representation of visual data for compression and compressed sensing

Miandji

2018

Linköping Studies in Science and Technology. Dissertations

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The cover of this thesis depicts the sparse representation of a light field. From the back side to front, a light field is divided into small elements, then these elements (signals) are projected onto an ensemble of two 5D dictionaries to produce a sparse coefficient vector. I hope that the reader can deduce where the dimensionality of those Rubik's Cube looking matrices come from after reading Chapter 3 of this thesis. The light field used on the cover is a part of the Stanford Lego Gantry database (http://lightfield.stanford.edu/)

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Section: Summary and Future Workmentioning

confidence: 99%

Sparse representation of visual data for compression and compressed sensing

Miandji

2018

Linköping Studies in Science and Technology. Dissertations

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“…This stage is analogous to the sparse coding stage in the dictionary learning and TL algorithms [4][5][6][7][8][12][13][14][15][16][17]. In this stage, the coefficients of representation and hence the probability distribution of the representation of the signals are identified by iteratively minimising the entropy of representation.…”

Section: Stage Imentioning

confidence: 99%

“…This paper discusses methods that generate a sparsifying transform for a class of signals from the signals itself. The proposals in this paper are motivated by the work reported in [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. This section describes the proposals in [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] briefly.…”

Section: Introductionmentioning

confidence: 99%

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Entropy‐based transform learning algorithms

Parthasarathy

Abhilash

2018

IET signal process.

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“…With the rise of big data and the rapid development of artificial intelligence, compressive sensing (CS) has been widely used and has obtained state-of-the-art results in multiple fields such as machine learning, neuroscience, signal processing, image and audio processing, classification, and statistics [23]- [25] because its features are flexibility, sparse and super-resolution. CS mainly consists two parts including sparse coding and dictionary design.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Performance Degradation Assessment Based on Convolutional Sparse Combination Learning

Huang

Cheng

2019

IEEE Access

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A novel bearing performance degradation assessment method based on convolutional sparse coding and combination learning is proposed in this paper, which can avoid the impact of the traditional features on the assessment results under complex operation conditions. The vibration signal can be decomposed into the convolution of the kernel sets and their corresponding sparse solutions using convolutional sparse coding. The learned kernel sets based on the training signal samples are the comprehensive embodiment of the information related to the operational complexity and the bearing healthy state, and the corresponding sparse solutions indicate the energy of the kernel activation. A simulation experiment of bearing signal proves that the activation energy of the kernels, which are more related to bearing healthy, will rise with the increase of the bearing degradation degree; meanwhile, the error of reconstructed signal based on the learned kernel sets and original signal will decrease since the description effect of convolutional sparse coding to the signal will be enhanced with the periodic strengthen of signal caused by the fault. Thus, an index based on the kernel sparse norms and the errors between the original and reconstructed signals has been proposed to evaluate the degradation degree of the bearing in this paper. On the other hand, combination learning will be fused into the convolutional sparse coding to improve the real-time performance of the index calculation and obtain the best assessment result in the testing part by dividing the kernel dictionary set into multiple sub-dictionaries. A bearing run-to-fail experiment is analyzed to verify the validity of the proposed method. The testing results show that the proposed assessment index can clearly detect the initial fault, serve fault and failure of the bearing in the whole life, and the proposed method is more self-adaptive and sensitive to the fault degree. In addition, it is verified that the time consumption of the combination dictionaries is less than that of a single dictionary set.

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ℓ1-K-SVD: A robust dictionary learning algorithm with simultaneous update

Cited by 35 publications

References 37 publications

Sparse representation of visual data for compression and compressed sensing

Sparse representation of visual data for compression and compressed sensing

Entropy‐based transform learning algorithms

Rolling Bearing Performance Degradation Assessment Based on Convolutional Sparse Combination Learning

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