A striking difference between the folk-narrative genres of legend and folktale is how the human characters respond to supernatural, otherworldly, or uncanny beings such as ghosts, gods, dwarves, giants, trolls, talking animals, witches, and fairies....
Dimensionality reduction methods can be used to project high-dimensional data into low-dimensional space. If the output space is restricted to two dimensions, the result is a scatter plot whose goal is to present insightful visualizations of distance...
This research paper focuses on dimensionality reduction, which is a major subproblem in any data processing operation. Dimensionality reduction based on principal components is the most used methodology. Our paper examines three heuristics, namely Ka...
The curse of dimensionality causes the well-known and widely discussed problems for machine learning methods. There is a hypothesis that using the Manhattan distance and even fractional
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...
High-dimensional tabular data are common in biomedical and clinical research, yet conventional machine learning methods often struggle in such settings due to data scarcity, feature redundancy, and limited generalization. In this study, we systematic...
With the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth’s surface objects. However, the...
This paper presents a novel psychological model of the socio-cognitive management of uncertainty, the semiotic dimensional model (SDM). The SDM claims that uncertainty increases the momentum of affect-laden meanings in meaning-making. This is so beca...
In brain–computer interface (BCI)-based motor imagery, the symmetric positive definite (SPD) covariance matrices of electroencephalogram (EEG) signals with discriminative information features lie on a Riemannian manifold, which is currently att...
As a core component of an aero-engine, the aerodynamic performance of the nacelle is essential for the overall performance of an aircraft. However, the direct design of a three-dimensional (3D) nacelle is limited by the complex design space consistin...
Gaussian Mixture Models (GMMs) are used in many traditional expert systems and modern artificial intelligence tasks such as automatic speech recognition, image recognition and retrieval, pattern recognition, speaker recognition and verification, fina...
Graph-based embedding methods receive much attention due to the use of graph and manifold information. However, conventional graph-based embedding methods may not always be effective if the data have high dimensions and have complex distributions. Fi...
Full-body movement involving multi-segmental coordination has been essential to our evolution as a species, but its study has been focused mostly on the analysis of one-dimensional data. The field is poised for a change by the availability of high-de...
Due to the wide variety of network services, many different types of protocols exist, producing various packet features. Some features contain irrelevant and redundant information. The presence of such features increases computational complexity and...
Domain adaptation manages to learn a robust classifier for target domain, using the source domain, but they often follow different distributions. To bridge distribution shift between the two domains, most of previous works aim to align their feature...
Equalisation is one of the most commonly-used tools in sound production, allowing users to control the gains of different frequency components in an audio signal. In this paper we present a model for mapping a set of equalisation parameters to a redu...
Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial di...
Dimensionality reduction is one of the most used transformations of data and plays a critical role in maintaining meaningful properties while transforming data from high- to low-dimensional spaces. Previous studies, e.g., on image analysis, comparing...
Synthetic metalloporphyrin complexes are often used as analogues of natural systems, and they can be used for the preparation of new Solid Coordination Frameworks (SCFs). In this work, a series of six metalloporphyrinic compounds constructed from dif...
In the Internet era, network malicious intrusion behaviors occur frequently and network intrusion detection is increasingly in demand. Addressing the challenges of high-dimensional data, nonlinearity and noisy network traffic data in network intrusio...
The dimensionality of parameters and variables is a fundamental issue in physics but is mostly ignored from a mathematical point of view. Difficulties arising from dimensional inconsistency are overcome by scaling analysis and, often, both concepts,...
Classification performances for some classes of electrocardiographic (ECG) and electroencephalographic (EEG) signals processed to dimensionality reduction with different degrees are investigated. Results got with various classification methods are gi...
The key to high-dimensional clustering lies in discovering the intrinsic structures and patterns in data to provide valuable information. However, high-dimensional clustering faces enormous challenges such as dimensionality disaster, increased data s...
Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microar...
Properties of data distributions can be assessed at both global and local scales. At a highly localized scale, a fundamental measure is the local intrinsic dimensionality (LID), which assesses growth rates of the cumulative distribution function with...
Simulation-based optimization design is becoming increasingly important in engineering. However, carrying out multi-point, multi-variable, and multi-objective optimization work is faced with the “Curse of Dimensionality”, which is highly...
Proper orthogonal decomposition (POD) is a widely used linear dimensionality reduction technique, but it often fails to capture critical features in complex nonlinear flows. In contrast, clustering methods are effective for nonlinear feature extracti...
The classification of hyperspectral remote sensing images is difficult due to the curse of dimensionality. Therefore, it is necessary to find an effective way to reduce the dimensions of such images. The Relief-F method has been introduced for superv...
Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good...
Due to the spectral complexity and high dimensionality of hyperspectral images (HSIs), the processing of HSIs is susceptible to the curse of dimensionality. In addition, the classification results of ground truth are not ideal. To overcome the proble...
The high dimensionality of real-life datasets is one of the biggest challenges in the machine learning field. Due to the increased need for computational resources, the higher the dimension of the input data is, the more difficult the learning task w...
Euclidean distance between instances is widely used to capture the manifold structure of data and for graph-based dimensionality reduction. However, in some circumstances, the basic Euclidean distance cannot accurately capture the similarity between...
Most well-known supervised dimensionality reduction algorithms suffer from the curse of dimensionality while handling high-dimensional sparse data due to ill-conditioned second-order statistics matrices. They also do not deal with multi-modal data pr...
Dimensionality reduction (DR) is an essential pre-processing step for hyperspectral image processing and analysis. However, the complex relationship among several sample clusters, which reveals more intrinsic information about samples but cannot be r...
Next-generation mobile communications networks will have to cope with an extraordinary amount and variety of network performance indicators, causing an increase in the storage needs of the network databases and the degradation of the management funct...
This work explores neural approximation for nonlinear dimensionality reduction mapping based on internal representations of graph-organized regular data supports. Given training observations are assumed as a sample from a high-dimensional space with...
Hyperspectral datasets provide explicit ground covers with hundreds of bands. Filtering contiguous hyperspectral datasets potentially discriminates surface features. Therefore, in this study, a number of spectral bands are minimized without losing or...
In order to explore complex structures and relationships hidden in data, plenty of graph-based dimensionality reduction methods have been widely investigated and extended to the multi-view learning field. For multi-view dimensionality reduction, the...
Since the first report of graphene, thin two-dimensional (2D) nanomaterials with atomic or molecular thicknesses have attracted great research interest for gas sensing applications. This was due to the distinctive physical, chemical, and electronic p...
Visual analytics are becoming increasingly important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one h...
Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smart...
Dimensionality reduction has always been a major problem for handling huge dimensionality datasets. Due to the utilization of labeled data, supervised dimensionality reduction methods such as Linear Discriminant Analysis tend achieve better classific...
The data from patients with severe brain injuries show complex brain functions. Due to the difficulties associated with these complex data, computational modeling is an especially useful tool to examine the structure–function relationship in th...
The label learning mechanism is challenging to integrate into the training model of the multi-label feature space dimensionality reduction problem, making the current multi-label dimensionality reduction methods primarily supervision modes. Many meth...
Vectors are a key type of geospatial data, and their discretization, which involves solving the problem of generating a discrete line, is particularly important. In this study, we propose a method for constructing a discrete line mathematical model f...
As a system becomes more complex, at first, its description and analysis becomes more complicated. However, a further increase in the system’s complexity often makes this analysis simpler. A classical example is Central Limit Theorem: when we have a...
Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neigh...
Psychometric network analysis (PNA) has been gaining great popularity over the past decade. As a promising dimensionality assessment method, existing research has shown that PNA is able to outperform traditional methods such as exploratory factor ana...
Dimensionality reduction techniques are crucial for enabling deep learning driven quantitative structure-activity relationship (QSAR) models to navigate higher dimensional toxicological spaces, however the use of specific techniques is often arbitrar...
The O*NET database is an online repository of detailed information on the knowledge and skill requirements of thousands of jobs across the United States. Thus, it is a valuable resource for test developers who want to target cognitive and other abili...
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