Principal Component Analysis

Mishra, Sidharth Prasad; Sarkar, Uttam Kumar; Taraphder, Subhash; Datta, Sanjoy; Swain, Devi; Saikhom, Reshma; Panda, Sasmita; Laishram, Menalsh

doi:10.5455/ijlr.20170415115235

Cited by 165 publications

(83 citation statements)

References 2 publications

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“…Principal component analysis (PCA) was performed using the data matrix that comprised the recorded data of the physicochemical and environmental parameters and the prevalence of Salmonella spp. in order to identify the most significant inter-correlated variables (Sidharth et al, 2017). Subsequently, the first two principal components (PCs) were interpreted with the aid of biplots, which provide a visual representation of the correlation between the physicochemical and environmental parameters with the presence of Salmonella spp.…”

Section: Discussionmentioning

confidence: 99%

Prevalence, Distribution, and Diversity of Salmonella Strains Isolated From a Subtropical Lake

et al. 2020

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This study investigated the prevalence, serovar distribution, antimicrobial resistance, and pulsed field gel electrophoresis (PFGE) typing of Salmonella enterica isolated from Lake Zapotlán, Jalisco, Mexico. Additionally, the association of the presence of Salmonella with physicochemical and environmental parameters was analyzed using Pearson correlation analysis and principal component analysis (PCA). Salmonella spp. were identified in 19 of 63 (30.15%) samples. The prevalence of Salmonella was positively correlated with air temperature, electrical conductivity, pH, and dissolved oxygen and negatively correlated with relative humidity, water temperature, turbidity, and precipitation. The predominant serotype identified was Agona (68.48%), followed by Weltevreden (5.26%), Typhimurium (5.26%), and serogroup B (21.05%). Overall, the highest detected antimicrobial resistance was toward colistin (73.68%), followed by sulfamethoxazole (63.15%), tetracycline (57.89%), nalidixic acid (52.63%), and trimethoprim (52.63%). All Salmonella strains were genetically diverse, with a total of 11 XbaI and four BlnI profiles on PFGE. The use of these two enzymes allowed differentiate strains of Salmonella of the same serotype. The results obtained in this study contribute to a better understanding of the Salmonella spp. ecology in an endorheic subtropical lake and provide information for decision makers to propose and implement effective strategies to control point and non-point sources of pathogen contamination.

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Section: Discussionmentioning

confidence: 99%

Prevalence, Distribution, and Diversity of Salmonella Strains Isolated From a Subtropical Lake

et al. 2020

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“…The second method is to combine data at its lowerdimensional latent subspace [106]. Statistical solutions such as Principal Component Analysis [107], Independent Component Analysis [108], and Canonical Component Analysis [109] are proposed for fusion by reducing the data dimensions. Early fusion is applied and performed on unprocessed raw data.…”

Section: A Typical Challenges In Multimodal Settingmentioning

confidence: 99%

“…Deep multimodal fusion performance is improved by reducing the data dimensions. After constructing a shared representation layer, PCA [107] and auto encoders [52] are used. Hybrid data fusion is far superior to early and late fusion.…”

Section: A Typical Challenges In Multimodal Settingmentioning

confidence: 99%

A Review on Explainability in Multimodal Deep Neural Nets

2021

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Artificial Intelligence techniques powered by deep neural nets have achieved much success in several application domains, most significantly and notably in the Computer Vision applications and Natural Language Processing tasks. Surpassing human-level performance propelled the research in the applications where different modalities amongst language, vision, sensory, text play an important role in accurate predictions and identification. Several multimodal fusion methods employing deep learning models are proposed in the literature. Despite their outstanding performance, the complex, opaque and black-box nature of the deep neural nets limits their social acceptance and usability. This has given rise to the quest for model interpretability and explainability, more so in the complex tasks involving multimodal AI methods. This paper extensively reviews the present literature to present a comprehensive survey and commentary on the explainability in multimodal deep neural nets, especially for the vision and language tasks. Several topics on multimodal AI and its applications for generic domains have been covered in this paper, including the significance, datasets, fundamental building blocks of the methods and techniques, challenges, applications, and future trends in this domain.INDEX TERMS deep multimodal learning, explainable AI, interpretability, survey, trends, vision and language research, XAI.

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“…Principal Component Analysis (PCA) is a technique to transforms several possibly correlated variables into a smaller number of variables called principal components [18]. PCA technique has many goals, including finding relationships between observations, extracting the most important information from the data, outlier detection and removal, and reducing the dimension of the data by keeping only the important information [19].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Frequency Band and PCA Feature Comparison for EEG Signal Classification

Pratama

Kesiman

Gunadi

2021

LKJITI

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The frequency band method is popular in signal processing; this method separates EEG signals into five bands of frequency. Besides the frequency band, the recent research show PCA method gives a good result to classify digits number from EEG signal. Even PCA give a good accuracy to classify digit number from EEG signal, but there are no research shows which one yielded better accuracy between PCA and frequency band to classify digit number from EEG signals. This paper presents the comparison between those methods using secondary data from MindBigData (MDB). The result shows that the frequency band and PCA achieve 9% and 12,5% on average accuracy with the EPOC dataset. The paired Wilcoxon test produces a significant difference in accuracy between methods in the digit classification problem. Experiment with Muse dataset provides 31% accuracy with frequency band method and 24,8% with PCA method. The result is competitive compared to other experiments to classify digit numbers from EEG signals. In conclusion, there is no winner between the two methods since no method fits both datasets used in this research.

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Principal Component Analysis

Abstract: Principal component analysis (PCA) is a multivariate technique that analyzes a data

Cited by 165 publications

References 2 publications

Prevalence, Distribution, and Diversity of Salmonella Strains Isolated From a Subtropical Lake

Prevalence, Distribution, and Diversity of Salmonella Strains Isolated From a Subtropical Lake

A Review on Explainability in Multimodal Deep Neural Nets

Frequency Band and PCA Feature Comparison for EEG Signal Classification

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