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Tebogo Bokaba, Wesley Doorsamy and Babu Sena Paul
Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and ...
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Olga Kurasova, Arnoldas Bud?ys and Viktor Medvedev
As artificial intelligence has evolved, deep learning models have become important in extracting and interpreting complex patterns from raw multidimensional data. These models produce multidimensional embeddings that, while containing a lot of informatio...
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Bardia Rafieian, Pedro Hermosilla and Pere-Pau Vázquez
In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim ...
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Li-Na Wang, Guoqiang Zhong, Yaxin Shi and Mohamed Cheriet
Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been propo...
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Evandro S. Ortigossa, Fábio Felix Dias and Diego Carvalho do Nascimento
The exploration and analysis of multidimensional data can be pretty complex tasks, requiring sophisticated tools able to transform large amounts of data bearing multiple parameters into helpful information. Multidimensional projection techniques figure a...
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Marcelo Chan Fu Wei, Ricardo Canal Filho, Tiago Rodrigues Tavares, José Paulo Molin and Afrânio Márcio Corrêa Vieira
To obtain a better performance when modeling soil spectral data for attribute prediction, researchers frequently resort to data pretreatment, aiming to reduce noise and highlight the spectral features. Even with the awareness of the existence of dimensio...
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Shingchern D. You and Ming-Jen Hung
This paper studies the use of three different approaches to reduce the dimensionality of a type of spectral?temporal features, called motion picture expert group (MPEG)-7 audio signature descriptors (ASD). The studied approaches include principal compone...
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Nikita Andriyanov
The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, ...
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Parag C. Pendharkar
Dimensionality reduction research in data envelopment analysis (DEA) has focused on subjective approaches to reduce dimensionality. Such approaches are less useful or attractive in practice because a subjective selection of variables introduces bias. A c...
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A simple model was built to predict the national and regional electricity demand by sectors under China’s new normal situation. In the model, the data dimensionality reduction method and the Grey model (GM(1,1)) were combined and adopted to disaggr...
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Fedor Krasnov
Pág. 79 - 84
Visualization of multidimensional data is the most important stage of data research. Often, decisions on the further stages of the study are made from the flat view of the data based on "rough proportions". High visibility and persuasiveness of represent...
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Leonid Safonov
Pág. 109 - 112
Unsupervised anomaly detection in high-dimensional data is an important subject of research in theoretical machine learning and applied areas. One of important applications is anomaly detection in network traffic data, which can be useful for preventing ...
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Muhammad Jawad, Jens Soltwisch, Klaus Dreisewerd and Lars Linsen
Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of t...
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B. Y. Bodnar,A. B. Ochkasov,T. S. Grusheckyna,Y. B. Bodnar
Pág. 45 - 59
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Lorena Salazar-Llano and Camilo Bayona-Roa
One challenging problem is the representation of three-dimensional datasets that vary with time. These datasets can be thought of as a cloud of points that gradually deforms. However, point-wise variations lack information about the overall deformation p...
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Xiaoling Tao, Deyan Kong, Yi Wei and Yong Wang
Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditi...
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Uraiwan Buatoom and Muhammad Usman Jamil
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 propos...
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Phattara Khumprom, David Grewell and Nita Yodo
Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM)...
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Robert A. N. de Oliveira and Methanias C. Junior
Stemming algorithms are commonly used during textual preprocessing phase in order to reduce data dimensionality. However, this reduction presents different efficacy levels depending on the domain that it is applied to. Thus, for instance, there are repor...
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Danfeng Liu, Liguo Wang and Jón Atli Benediktsson
Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approac...
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