Principal Component Analysis: A Natural Approach to Data Exploration

Gewers, Felipe L.; Ferreira, Gustavo R.; de Arruda, Henrique F.; Silva, Filipi N.; Comin, Cesar H.; Amancio, Diego R.; Costa, Luciano da F.

doi:10.48550/arxiv.1804.02502

Cited by 7 publications

(7 citation statements)

References 187 publications

(239 reference statements)

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“…66 Data standardization is recommended when the raw variables have been measured on a significantly different scale. 67 Other distance-based algorithms such as k-NN, Kmeans, and SVM are also dramatically affected by the scale of the features, while tree-based algorithms and Nai ̈ve Bayes classifiers are less affected. Unfortunately, data normalization is often ignored or not clarified in the environmental application practice of ML.…”

Section: ■ Concluding Remarksmentioning

confidence: 99%

Data-Driven Machine Learning in Environmental Pollution: Gains and Problems

Liu

Lü

Zhang

et al. 2022

Environ. Sci. Technol.

228

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The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. In the past decade, the unprecedented accumulation of data, the development of high-performance computing power, and the rise of diverse machine learning (ML) methods provide new opportunities for environmental pollution research. The ML methodology has been used in satellite data processing to obtain ground-level concentrations of atmospheric pollutants, pollution source apportionment, and spatial distribution modeling of water pollutants. However, unlike the active practices of ML in chemical toxicity prediction, advanced algorithms such as deep neural networks in environmental process studies of pollutants are still deficient. In addition, over 40% of the environmental applications of ML go to air pollution, and its application range and acceptance in other aspects of environmental science remain to be increased. The use of ML methods to revolutionize environmental science and its problem-solving scenarios has its own challenges. Several issues should be taken into consideration, such as the tradeoff between model performance and interpretability, prerequisites of the machine learning model, model selection, and data sharing.

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Section: ■ Concluding Remarksmentioning

confidence: 99%

Data-Driven Machine Learning in Environmental Pollution: Gains and Problems

Liu

Lü

Zhang

et al. 2022

Environ. Sci. Technol.

228

View full text Add to dashboard Cite

show abstract

“…The running index (last 3 months) of the newly created image is maintained for later search and retrieval [5]. Due to the continuous nature of the application, hash-based indexing is the preferred choice over the technique of collectively learning representations from static datasets such as Principal component analysis (PCA) [3]. As the catalogue changes, so do the best key components, which require frequent recalculations.…”

Section: Similar Image Retrieval (Sir)mentioning

confidence: 99%

Identical Image Retrieval using Deep Learning

Nath¹,

Nayak²

2022

Preprint

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In recent years, we know that the interaction with images has increased. Image similarity involves fetching similar-looking images abiding by a given reference image. The target is to find out whether the image searched as a query can result in similar pictures. We are using the BigTransfer Model, which is a state-of-art model itself. BigTransfer(BiT) is essentially a ResNet but pre-trained on a larger dataset like ImageNet and ImageNet-21k with additional modifications. Using the fine-tuned pre-trained Convolution Neural Network Model, we extract the key features and train on the K-Nearest Neighbor model to obtain the nearest neighbor. The application of our model is to find similar images, which are hard to achieve through text queries within a low inference time. We analyse the benchmark of our model based on this application.

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“…In order to better understand the variation of the diversity with the parameters, we flattened the obtained values of D and calculated the respective PCA (Principal Component Analysis) projection [31,32] (see Figure 4). An interesting result concerns the separation of the cases into three regions in terms of the average degree, identified by respective ellipses in Figure 4.…”

Section: A No Reconnection Constraintmentioning

confidence: 99%

Contrarian effects and echo chamber formation in opinion dynamics

Arruda¹,

Benatti²,

Silva³

et al. 2021

J. Phys. Complex.

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The relationship between the topology of a network and specific types of dynamics unfolding in networks constitutes a subject of substantial interest. One type of dynamics that has attracted increasing attention because of its several potential implications is opinion formation. A phenomenon of particular importance, known to take place in opinion formation, is echo chambers’ appearance. In the present work, we approach this phenomenon, while emphasizing the influence of contrarian opinions in a multi-opinion scenario. To define the contrarian opinion, we considered the underdog effect, which is the eventual tendency of people to support the less popular option. We also considered an adaptation of the Sznajd dynamics with the possibility of friendship rewiring, performed on several network models. We analyze the relationship between topology and opinion dynamics by considering two measurements: opinion diversity and network modularity. Two specific situations have been addressed: (i) the agents can reconnect only with others sharing the same opinion; and (ii) same as in the previous case, but with the agents reconnecting only within a limited neighborhood. This choice can be justified because, in general, friendship is a transitive property along with subsequent neighborhoods (e.g., two friends of a person tend to know each other). As the main results, we found that the underdog effect, if strong enough, can balance the agents’ opinions. On the other hand, this effect decreases the possibilities of echo chamber formation. We also found that the restricted reconnection case reduced the chances of echo chamber formation and led to smaller echo chambers.

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Principal Component Analysis: A Natural Approach to Data Exploration

Cited by 7 publications

References 187 publications

Data-Driven Machine Learning in Environmental Pollution: Gains and Problems

Data-Driven Machine Learning in Environmental Pollution: Gains and Problems

Identical Image Retrieval using Deep Learning

Contrarian effects and echo chamber formation in opinion dynamics

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