New semi-supervised classification using a multi-modal feature joint <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml9" display="inline" overflow="scroll" altimg="si9.gif"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>21</mml:mn></mml:mrow></mml:msub></mml:math>-norm based sparse representation

Cui, Yan; Jiang, Jielin; Lai, Zhihui; Hu, Zuojin; Jiang, Yuquan

doi:10.1016/j.image.2018.03.005

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…noise contamination including multiplicative speckle noise and additive white Gaussian noise, limited training resource, resolution variance, partial occlusion etc. [7–47]. In practice, however, it seems that most crucial EOCs are adding speckle noise, different depression angles and a limited number of the training samples.…”

Section: Resultsmentioning

confidence: 99%

“…Considering the above facts, efforts have been made to develop SRC algorithms. A summary can be presented as follows: (i) using kernel method to transfer samples to new higher dimension spaces where classes can be linearly discriminated [29–31], (ii) utilising manifold learning [32–34], (iii) fusing SRC with other classification methods [24, 35], (iv) using l2‐norm [36, 37] or other norms [38, 39] instead of l1‐norm in SRC, (v) acquiring a dictionary via DL methods instead of using training samples could be very effective in the SR and SRC results [20, 40–42]. Based on the latter facts, and making use of the Fisher criterion, Zhang and co‐authors [43, 44] introduced Fisher discriminative DL (FDDL).…”

Section: Introductionmentioning

confidence: 99%

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Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Shafie¹,

Moallem²,

Sabahi³

2020

IET Radar, Sonar & Navigation

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Shafie¹,

Moallem²,

Sabahi³

2020

IET Radar, Sonar & Navigation

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“…where W 1 ∈ R d×m and W 2 ∈ R m×m are the coefficient matrices of each part, and λ 1 is the regularization parameter. As l 2,1 -norm is commonly employed as a sparse regularizer [35], where || W 1 || is a regularizer then forces sparsity on the matrix W .…”

Section: Label Distribution Learningmentioning

confidence: 99%

Weakly-supervised label distribution feature selection via label-specific features and label correlation

Shu,

Hu,

Qian

2024

Preprint

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Label Distribution Learning (LDL) stands out as an innovative method to address the challenges posed by label ambiguity. Current LDL algorithms are predominantly designed for datasets with comprehensive supervised information. However, in real-world scenarios, it’s common to encounter partial missing labels within the label space. This phenomenon disrupts the structure and correlation between labels, posing a challenge to the precise design of learning algorithms. Furthermore, in label distribution learning, it also faces the effect of high feature dimensionality. To tackle this, adopting pre-processing methods like feature selection becomes crucial, aiming to trim down the data dimensionality. Motivated by this, a weakly-supervised label distribution feature selection algorithm based on label correlation is proposed in this paper. First, to handle weakly-supervised label distribution data, a two-stage incomplete label distribution learning method (IncomLDLTS) to recover missing labels by exploiting label-independent prediction based on label-specific features is proposed. Second, a minimum correlation label feature selection algorithm (MCLFS) to enhance the performance for complete label distribution data is designed, which employs the interaction information metric to explore the label correlation to obtain a minimum correlation label, then features about each label based on label-specific features and feature redundancy are captured. Finally, based on six representative evaluation metrics, our experiments across 14 datasets affirm the effectiveness of our approach, not only in restoring missing labels but also in choosing essential features, leading to enhanced classification accuracy.

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Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis

Zhou

Bao

et al. 2019

Med Biol Eng Comput

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New semi-supervised classification using a multi-modal feature joint L21-norm based sparse representation

Cited by 5 publications

References 25 publications

Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Weakly-supervised label distribution feature selection via label-specific features and label correlation

Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis

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