Principal component analysis

Kherif, Ferath; Latypova, Adeliya

doi:10.1016/b978-0-12-815739-8.00012-2

Cited by 180 publications

(97 citation statements)

References 11 publications

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“…Viewing the table, it is evident that CDTRL yields performance superior to the others. [13] 93.33% 2DPCA [14] 94.17% LDA [15] 95.00% 2DLDA [16] 95.83% CCA [5] 95.83% DCCA [24] 98.33% LCCA [25] 96.67% 2DCCA [9] 97.50% L2DCCA [17] 98.01% The proposed CDTRL 100.00%…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis

Gao

Guan

2020

IEEE Signal Process. Lett.

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As an effective tool for two-dimensional data analysis, two-dimensional canonical correlation analysis (2DCCA) is not only capable of preserving the intrinsic structural information of original two-dimensional (2D) data, but also reduces the computational complexity effectively. However, due to the unsupervised nature, 2DCCA is incapable of extracting sufficient discriminatory representations, resulting in an unsatisfying performance. In this letter, we propose a complete discriminative tensor representation learning (CDTRL) method based on linear correlation analysis for analyzing 2D signals (e.g. images). This letter shows that the introduction of the complete discriminatory tensor representation strategy provides an effective vehicle for revealing and extracting the discriminant representations across the 2D data sets, leading to improved results. Experimental results show that the proposed CDTRL outperforms state-ofthe-art methods on the evaluated data sets.

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Section: Experimental Results and Analysismentioning

confidence: 99%

A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis

Gao

Guan

2020

IEEE Signal Process. Lett.

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“…PCA determines a smaller set of artificial variables that will represent the variance of a series of observed variables the calculated artificial variable is called the main components. The main component is used as a predictor variable or criterion in another analysis [17], [18].…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysis on Twitter by Using Maximum Entropy and Support Vector Machine Method

Cindo

Rini

Ermatita

2020

Sinergi

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With the advancement of social media and its growth, there is a lot of data that can be presented for research in social mining. Twitter is a microblogging that can be used. In this event, a lot of companies used the data on Twitter to analyze the satisfaction of their customer about product quality. On the other hand, a lot of users use social media to express their daily emotions. The case can be developed into a research study that can be used both to improve product quality, as well as to analyze the opinion on certain events. The research is often called sentiment analysis or opinion mining. While The previous research does a particularly useful feature for sentiment analysis, but it is still a lack of performance. Furthermore, they used Support Vector Machine as a classification method. On the other hand, most researchers found another classification method, which is considered more efficient such as Maximum Entropy. So, this research used two types of a dataset, the general opinion data, and the airline's opinion data. For feature extraction, we employ four feature extraction, such as pragmatic, lexical-grams, pos-grams, and sentiment lexical. For the classification, we use both of Support Vector Machine and Maximum Entropy to find the best result. In the end, the best result is performed by Maximum Entropy with 85,8% accuracy on general opinion data, and 92,6% accuracy on airlines opinion data.

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“…The reduction of 18 original variables into two constructed variables (PCs) is important in understanding the Si effect from all the joint variables, because the PCA is efficient in reducing many variables into smaller subspace with minimal information loss (Alkarkhi;Alqaraghuli, 2020;Saccenti;Camacho, 2020;Kherif;Latypova, 2020). Thus, PCA application was efficient in this research, since, with both PCs, it was possible to explain high proportions of s 2 (89%) with low information losses (11%).…”

Section: Formation Of Principal Components and Silicon Effectmentioning

confidence: 94%

Physiological adjustments, fiber yield and quality of colored cotton BRS Topázio cultivar under leaf silicon spraying

et al. 2021

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Abiotic stresses reduce the yield and quality of cotton production, however, this problem may be mitigated by exogenous elicitors. This study investigated physiological adjustments, improvement in productivity, and fiber yield and quality from naturally colored cotton BRS Topázio cultivar under leaf silicon spraying. Five doses of silicon (0, 50, 100, 150, and 200 mg L-1) were tested in a completely randomized design. The variables assessed were physiological, production, fiber yield and quality. Data were submitted to principal component analysis, multivariate and univariate variance, and multiple linear regression. Silicon promotes physiological adjustments, increased production, cotton fiber yield and quality. Optimum silicon dose varies depending on the variable response. Fiber quality from plants treated with silicon is within the expected pattern for this cultivar and the international standard D-4605 of the American Society for Testing and Materials. Therefore, planting BRS Topázio cultivar under silicon foliar applications may be recommended to improve the naturally colored fiber cotton production chain in the Brazilian semiarid region.

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Principal component analysis

Cited by 180 publications

References 11 publications

A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis

A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis

Sentiment Analysis on Twitter by Using Maximum Entropy and Support Vector Machine Method

Physiological adjustments, fiber yield and quality of colored cotton BRS Topázio cultivar under leaf silicon spraying

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