Toward More Efficient WMSN Data Search Combined FJLT Dimension Expansion With PCA Dimension Reduction

Xiao, Chenkai; Shao, Wenhao; Xiao, Ruliang

doi:10.1109/access.2020.2999484

Cited by 4 publications

(5 citation statements)

References 34 publications

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Section: Weighted Variable For Guidance Dimension Reduction Based On Pcamentioning

confidence: 99%

“…Nowadays, many studies focus on improving the dataset before selecting the feature with the most information by using PCA [16,[22][23][24][25]. Xiao et al (2020) used a low-distortion projection fast-Johnson-Lindenstrauss transform technique for dimensional expansion and then projected the data to a higher dimensional metric space before performing PCA [23]. Tavoli et al (2013) experimented with feature weighting based on keyword spotting with a document image retrieval system, and then weighted the features using weighted PCA [24].…”

Section: Weighted Variable For Guidance Dimension Reduction Based On Pcamentioning

confidence: 99%

See 1 more Smart Citation

Improving Classification Performance with Statistically Weighted Dimensions and Dimensionality Reduction

Buatoom

Jamil

2023

Applied Sciences

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In image classification, various techniques have been developed to enhance the performance of principal component analysis (PCA) dimension reduction techniques with guiding weighting features to remove redundant and irrelevant features. This study proposes the statistically weighted dimension technique based on three distribution-related class behaviors; collection-class, inter-class, and intra-class to enhance the feature-extraction ability before using PCA for feature selection. The data from the statistics-weighted dimension spaces is utilized to reduce dimensionality by reducing the large index data into smaller index data using PCA. The new principal component from the weighted training part by an unlabeled dataset is constructed and then the image is classified efficiently. Additionally, the weighting direction investigates the pros and cons of promoting and demoting to determine the worst or best option utilizing the exponents of three proposed weighted scheme. The experiment is conducted using three datasets, MNIST, E-MNIST, and F-MNIST, along with three image classification algorithms, logistic Regression, KNN, and SVM (RBF). The results clearly demonstrate that the statistically weighted dimension feature can improve the conventional classification accuracy in lower dimensions with an appropriate combination of weighting nearly 3% for the best solution on dimensionality reduction by more than 50%.

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Section: Weighted Variable For Guidance Dimension Reduction Based On Pcamentioning

confidence: 99%

Section: Weighted Variable For Guidance Dimension Reduction Based On Pcamentioning

confidence: 99%

Improving Classification Performance with Statistically Weighted Dimensions and Dimensionality Reduction

Buatoom

Jamil

2023

Applied Sciences

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“…Two performance indicators, accuracy and 1-off accuracy, are used to evaluate the result of age classification. Accuracy is defined as the proportion of all correctly classified results in the total number of samples, as shown in Equation (26), where TP, TN, FN, and TN represent true positive, true negative, false negative, and true negative, respectively. 1-off accuracy is defined as the proportion of all correctly classified results in the total number of samples when the classified result is within a domain of the correct label.…”

Section: Assessment Of the Proposed Algorithmmentioning

confidence: 99%

“…Xiao & Yin focussed on the problems with high feature dimensions and complex computation. They proposed a feature extraction algorithm, PCA-SIFT, based on dimension reduction by introducing the PCA algorithm [26]. The PCA-SIFT greatly improved face recognition.…”

Section: Feature Selectionmentioning

confidence: 99%

Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm

Wang

Zhang

et al. 2022

CAAI Trans on Intel Tech

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Aiming at the problem of long time-consuming and low accuracy of existing age estimation approaches, a new age estimation method using Gabor feature fusion, and an improved atomic search algorithm for feature selection is proposed. Firstly, texture features of five scales and eight directions in the face region are extracted by Gabor wavelet transform. The statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor scales. Secondly, a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing (CIASO-SA) is presented, which is based on an improved atomic search algorithm and the simulated annealing algorithm. Besides, the CIASO-SA algorithm introduces a chaos mechanism during atomic initialisation, significantly improving the convergence speed and accuracy of the algorithm. Finally, a support vector machine (SVM) is used to get classification results of the age group. To verify the performance of the proposed algorithm, face images with three resolutions in the Adience dataset are tested. Using the Gabor real part fusion feature at 48 � 48 resolution, the average accuracy and 1-off accuracy of age classification exhibit a maximum of 60.4% and 85.9%, respectively. Obtained results prove the superiority of the proposed algorithm over the state-of-the-art methods, which is of great referential value for application to the mobile terminals.

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“…The authors use PCA to reduce the dimension of false data injection attack data samples in power grid [17] . Moreover, Chen et al use CT-PAC to reduce the sample data of human activity recognition, and both methods achieve good results [18]. However, if the data is nonlinear, the above methods are difficult to mine the primary features of the data set, and they cannot effectively reduce the data.…”

Section: Introductionmentioning

confidence: 99%

Online Network traffic anomaly detection method combining OS-ELM and SADE

Dai,

Zhao,

Huang

et al. 2024

IEEE Access

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Network traffic anomaly detection methods can detect traffic that is significantly different from normal traffic by analyzing network traffic, and are seen as an effective means to detect unknown new attacks because they do not rely on static feature codes. However, most of the current network traffic anomaly detection methods have low accuracy and high false alarm rate. In this paper, an OS-ELM (Online Sequential Extreme Learning Machine) network traffic anomaly detection method is proposed. First, we use SMOTE to balance the data samples to improve the classification detection performance. In order to reduce the high dimensionality between data features and eliminate the redundancy, the Stacked Denoising Autoencoder (SDAE) network is adopt to reduce the dimension of the feature vectors. Finally, we test the real network traffic and analyze the effect of model structure and external noise on the detection performance, and the experimental results verify the correctness of our scheme. Compared with other detection methods based on data reconstruction, the proposed method has higher detection accuracy and better detection performance.

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Toward More Efficient WMSN Data Search Combined FJLT Dimension Expansion With PCA Dimension Reduction

Cited by 4 publications

References 34 publications

Improving Classification Performance with Statistically Weighted Dimensions and Dimensionality Reduction

Improving Classification Performance with Statistically Weighted Dimensions and Dimensionality Reduction

Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm

Online Network traffic anomaly detection method combining OS-ELM and SADE

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