Twin-Incoherent Self-Expressive Locality-Adaptive Latent Dictionary Pair Learning for Classification

Zhang, Zhao; Sun, Yulin; Wang, Yan; Zhang, Zheng; Zhang, Haijun; Liu, Guangcan; Wang, Meng

doi:10.1109/tnnls.2020.2979748

Cited by 38 publications

(15 citation statements)

References 47 publications

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“…SDRCF [120]. SDRCF is inspired by the sparse representation (SR) [56][57][58][59][60][61][62][63][64][65][66], which simultaneously incorporates the local geometrical structures of both the data and features into CF, and obtain a weight matrix. For a sample x and a matrix DN   containing the dictionary atoms in its columns, SR represents x using as few entries of as possible, defined as follows:…”

Section: Scf [127] and Rscf [127]mentioning

confidence: 99%

“…Because the RL methods can effectively simplify the complex input data, eliminate invalid information and extract useful information (or features) from observed inputs [45][46][47][48][49][50][51][52][53][54][55]. Classical RL approaches include feature extraction (FE) , sparse dictionary learning (SDL) [56][57][58][59][60][61][62][63][64][65][66], low-rank coding (LRC) [91][92][93][94][95][96][97][98][99][100][101][102][103][104], matrix factorization (MF) [1][2][3][4][5][6] [ [105][106][107][108][109][110][111]…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Concept Factorization: From Shallow to Deep Representation Learning

Zhang

et al. 2021

Information Processing & Management

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Section: Scf [127] and Rscf [127]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Concept Factorization: From Shallow to Deep Representation Learning

Zhang

et al. 2021

Information Processing & Management

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“…The acquisition of microblog features begins at the word level and moves to the sentence level. The introduction of the lexicon can add more task-related word information, leading to stronger semantics [15,16] . However, no lexicon designed for content credibility evaluation has yet been developed.…”

Section: Introductionmentioning

confidence: 99%

A Microblog Content Credibility Evaluation Model Based on Collaborative Key Points

Xing

Yao

2022

Preprint

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The spread of false content on microblogging platforms has created information security threats for users and platforms alike. The confusion caused by false content complicates feature selection during credibility evaluation. To solve this problem, a collaborative key point-based content credibility evaluation model, CECKP, is proposed in this paper. The model obtains the key points of the microblog text from the word level to the sentence level, then evaluates the credibility according to the semantics of the key points. In addition, a rumor lexicon constructed collaboratively during word-level coding strengthens the semantics of related words and solves the feature selection problem when using deep learning methods for content credibility evaluation. Experimental results show that, compared with the Att-BiLSTM model, the F1 score of the proposed model increases by 3.83% and 3.8% when the evaluation results are true and false respectively. The proposed model accordingly improves the performance of content credibility evaluation based on optimized feature selection.

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“…The development of deep learning [15,16,17] brings opportunities and challenges to the field of computer vision [18,19]. Some methods have been proposed in recent papers to obtain the discriminative details features of the local vehi-cle.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification

Wang

Peng

Jiang

et al. 2020

Preprint

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With the development of smart cities, urban surveillance video analysis will play a further significant role in intelligent transportation systems. Identifying the same target vehicle in large datasets from non-overlapping cameras should be highlighted, which has grown into a hot topic in promoting intelligent transportation systems. However, vehicle re-identification (re-ID) technology is a challenging task since vehicles of the same design or manufacturer show similar appearance. To fill these gaps, we tackle this challenge by proposing Triplet Center Loss based Part-aware Model (TCPM) that leverages the discriminative features in part details of vehicles to refine the accuracy of vehicle re-identification.TCPM base on part discovery is that partitions the vehicle from horizontal and vertical directions to strengthen the details of the vehicle and reinforce the internal consistency of the parts. In addition, to eliminate intra-class differences in local regions of the vehicle, we propose external memory modules to emphasize the consistency of each part to learn the discriminating features, which forms a global dictionary over all categories in dataset. In TCPM, triplet-center loss is introduced to ensure each part of vehicle features extracted has intra-class consistency and inter-class separability. Experimental results show that our proposed TCPM has an enormous preference over the existing state-of-the-art methods on benchmark datasets VehicleID and VeRi-776.

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Twin-Incoherent Self-Expressive Locality-Adaptive Latent Dictionary Pair Learning for Classification

Cited by 38 publications

References 47 publications

A Survey on Concept Factorization: From Shallow to Deep Representation Learning

A Survey on Concept Factorization: From Shallow to Deep Representation Learning

A Microblog Content Credibility Evaluation Model Based on Collaborative Key Points

Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification

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