Feature fusion: parallel strategy vs. serial strategy

Yang, Jian; Yang, Jing; Zhang, David; Lu, Junfeng

doi:10.1016/s0031-3203(02)00262-5

Cited by 400 publications

(178 citation statements)

References 20 publications

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“…In addition, no other components could be used to distinguish between particular visual stimuli objects with greater accuracy. Another notable finding was that the classification accuracy of multiple ERPs was superior to that of single ERP components (Wang et al 2012), which was also in line with previous studies (Yang et al 2003;Blankertz et al 2011;Kayser and Tenke 2003;Sajda et al 2010;Murphy et al 2008a). Our results further validated the complementarity of discriminative information from different ERP components.…”

Section: Discussion Further Discussion On Classification Resultssupporting

confidence: 90%

“…Complementary discrimination information between different ERP components has been studied extensively (Wang et al 2012;Yang et al 2003;Blankertz et al 2011;Kayser and Tenke 2003;Sajda et al 2010;Murphy et al 2008a). In addition to the complementarity of discriminative information from different ERPs, another question arose regarding information redundancy (i.e., whether information from a specific ERP component can be derived from other ERP components).…”

Section: Diversity Of Single Erp Components In Classificationmentioning

confidence: 99%

“…Existing ERP feature fusion for classification could be implemented using two basic strategies: serial and parallel (Yang et al 2003). In serial ERP fusion, all ERP feature sets were grouped together to form a longer feature.…”

Section: Introductionmentioning

confidence: 99%

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Classifying four-category visual objects using multiple ERP components in single-trial ERP

Qin

Wang

et al. 2016

Cogn Neurodyn

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Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiplekernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiplekernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.

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Section: Discussion Further Discussion On Classification Resultssupporting

confidence: 90%

Section: Diversity Of Single Erp Components In Classificationmentioning

confidence: 99%

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Classifying four-category visual objects using multiple ERP components in single-trial ERP

Qin

Wang

et al. 2016

Cogn Neurodyn

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“…In this experiment, we use the Concordia University CENPARMI handwritten numeral database [42], [44]. This database contains 6,000 samples of 10 numeral classes (each class has 600 samples).…”

Section: Experiments On Handwritten Digit Classificationmentioning

confidence: 99%

“…This database contains 6,000 samples of 10 numeral classes (each class has 600 samples). Here, our experiment is performed based on 256-dimensional Gabor transformation features [43], [44], which turned out to be effective for handwritten digit classification.…”

Section: Experiments On Handwritten Digit Classificationmentioning

confidence: 99%

KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition

Yang

Frangi

Yang

et al. 2005

IEEE Trans. Pattern Anal. Machine Intell.

736

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Abstract-This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based on this framework, the authors propose a complete kernel Fisher discriminant analysis (CKFD) algorithm. CKFD can be used to carry out discriminant analysis in "double discriminant subspaces." The fact that, it can make full use of two kinds of discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms.

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VTCNet: A Feature Fusion DL Model Based on CNN and ViT for the Classification of Cervical Cells

Li,

Que,

Zhang

et al. 2024

Int J Imaging Syst Tech

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Cervical cancer is a common malignancy worldwide with high incidence and mortality rates in underdeveloped countries. The Pap smear test, widely used for early detection of cervical cancer, aims to minimize missed diagnoses, which sometimes results in higher false‐positive rates. To enhance manual screening practices, computer‐aided diagnosis (CAD) systems based on machine learning (ML) and deep learning (DL) for classifying cervical Pap cells have been extensively researched. In our study, we introduced a DL‐based method named VTCNet for the task of cervical cell classification. Our approach combines CNN‐SPPF and ViT components, integrating modules like Focus and SeparableC3, to capture more potential information, extract local and global features, and merge them to enhance classification performance. We evaluated our method on the public SIPaKMeD dataset, achieving accuracies, precision, recall, and F1 scores of 97.16%, 97.22%, 97.19%, and 97.18%, respectively. We also conducted additional experiments on the Herlev dataset, where our results outperformed previous methods. The VTCNet method achieved higher classification accuracy than traditional ML or shallow DL models through this integration. Related codes: https://github.com/Camellia‐0892/VTCNet/tree/main.

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Feature fusion: parallel strategy vs. serial strategy

Cited by 400 publications

References 20 publications

Classifying four-category visual objects using multiple ERP components in single-trial ERP

Classifying four-category visual objects using multiple ERP components in single-trial ERP

KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition

VTCNet: A Feature Fusion DL Model Based on CNN and ViT for the Classification of Cervical Cells

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