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59,689 Results Found

  • Article
  • Open Access
6 Citations
6,251 Views
43 Pages

Analyzing Quality Measurements for Dimensionality Reduction

  • Michael C. Thrun,
  • Julian Märte and
  • Quirin Stier

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...

  • Article
  • Open Access
41 Citations
7,430 Views
31 Pages

30 September 2020

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 lp quasinorms (for p less than 1) can help to overcome the curse...

  • Article
  • Open Access
6 Citations
7,393 Views
20 Pages

Improving Dimensionality Reduction Projections for Data Visualization

  • Bardia Rafieian,
  • Pedro Hermosilla and
  • Pere-Pau Vázquez

4 September 2023

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...

  • Article
  • Open Access
121 Views
18 Pages

Overcoming the Curse of Dimensionality with Synolitic AI

  • Alexey Zaikin,
  • Ivan Sviridov,
  • Artem Sosedka,
  • Anastasia Linich,
  • Ruslan Nasyrov,
  • Evgeny M. Mirkes and
  • Tatiana Tyukina

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...

  • Article
  • Open Access
13 Citations
2,968 Views
40 Pages

A Study on Dimensionality Reduction and Parameters for Hyperspectral Imagery Based on Manifold Learning

  • Wenhui Song,
  • Xin Zhang,
  • Guozhu Yang,
  • Yijin Chen,
  • Lianchao Wang and
  • Hanghang Xu

25 March 2024

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...

  • Article
  • Open Access
8 Citations
3,534 Views
25 Pages

The Affective Regulation of Uncertainty: The Semiotic Dimensionality Model (SDM)

  • Sergio Salvatore,
  • Terri Mannarini,
  • Alessandro Gennaro,
  • Giovanna Celia,
  • Serena De Dominicis,
  • Raffaele De Luca Picione,
  • Salvatore Iuso,
  • Skaiste Kerušauskaitė,
  • Johann Roland Kleinbub and
  • Giulia Rocchi
  • + 4 authors

5 April 2023

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...

  • Article
  • Open Access
9 Citations
4,166 Views
18 Pages

23 March 2023

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...

  • Article
  • Open Access
5 Citations
2,676 Views
23 Pages

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...

  • Article
  • Open Access
4 Citations
2,659 Views
22 Pages

Measure of Similarity between GMMs Based on Geometry-Aware Dimensionality Reduction

  • Branislav Popović,
  • Marko Janev,
  • Lidija Krstanović,
  • Nikola Simić and
  • Vlado Delić

29 December 2022

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...

  • Review
  • Open Access
6 Citations
4,232 Views
19 Pages

Locality Sensitive Discriminative Unsupervised Dimensionality Reduction

  • Yun-Long Gao,
  • Si-Zhe Luo,
  • Zhi-Hao Wang,
  • Chih-Cheng Chen and
  • Jin-Yan Pan

12 August 2019

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...

  • Article
  • Open Access
1 Citations
1,513 Views
15 Pages

21 April 2025

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...

  • Article
  • Open Access
6 Citations
3,840 Views
14 Pages

A Hybrid Dimensionality Reduction for Network Intrusion Detection

  • Humera Ghani,
  • Shahram Salekzamankhani and
  • Bal Virdee

16 November 2023

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...

  • Article
  • Open Access
2 Citations
3,241 Views
18 Pages

Optimal Transport with Dimensionality Reduction for Domain Adaptation

  • Ping Li,
  • Zhiwei Ni,
  • Xuhui Zhu,
  • Juan Song and
  • Wenying Wu

3 December 2020

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...

  • Article
  • Open Access
19 Citations
7,205 Views
19 Pages

20 April 2016

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...

  • Article
  • Open Access
68 Citations
9,394 Views
18 Pages

13 September 2022

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...

  • Article
  • Open Access
1 Citations
2,325 Views
24 Pages

Dimensionality Reduction and Anomaly Detection Based on Kittler’s Taxonomy: Analyzing Water Bodies in Two Dimensional Spaces

  • Giovanna Carreira Marinho,
  • Wilson Estécio Marcílio Júnior,
  • Mauricio Araujo Dias,
  • Danilo Medeiros Eler,
  • Rogério Galante Negri and
  • Wallace Casaca

19 August 2023

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...

  • Article
  • Open Access
19 Citations
8,295 Views
17 Pages

Coordination and Crystallization Molecules: Their Interactions Affecting the Dimensionality of Metalloporphyrinic SCFs

  • Arkaitz Fidalgo-Marijuan,
  • Eder Amayuelas,
  • Gotzone Barandika,
  • Begoña Bazán,
  • Miren Karmele Urtiaga and
  • María Isabel Arriortua

15 April 2015

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...

  • Article
  • Open Access
1,223 Views
24 Pages

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...

  • Article
  • Open Access
2,392 Views
21 Pages

12 March 2022

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,...

  • Article
  • Open Access
18 Citations
4,441 Views
20 Pages

19 May 2021

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...

  • Article
  • Open Access
1 Citations
2,600 Views
16 Pages

Sparse Clustering Algorithm Based on Multi-Domain Dimensionality Reduction Autoencoder

  • Yu Kang,
  • Erwei Liu,
  • Kaichi Zou,
  • Xiuyun Wang and
  • Huaqing Zhang

14 May 2024

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...

  • Article
  • Open Access
6 Citations
9,472 Views
15 Pages

15 July 2008

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...

  • Feature Paper
  • Article
  • Open Access
13 Citations
5,034 Views
32 Pages

Local Intrinsic Dimensionality, Entropy and Statistical Divergences

  • James Bailey,
  • Michael E. Houle and
  • Xingjun Ma

30 August 2022

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...

  • Article
  • Open Access
19 Citations
4,038 Views
20 Pages

Dimensionality Reduction, Modelling, and Optimization of Multivariate Problems Based on Machine Learning

  • Mohammed Alswaitti,
  • Kamran Siddique,
  • Shulei Jiang,
  • Waleed Alomoush and
  • Ayat Alrosan

21 June 2022

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...

  • Article
  • Open Access
954 Views
29 Pages

17 June 2025

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...

  • Article
  • Open Access
32 Citations
3,554 Views
21 Pages

Partitioned Relief-F Method for Dimensionality Reduction of Hyperspectral Images

  • Jiansi Ren,
  • Ruoxiang Wang,
  • Gang Liu,
  • Ruyi Feng,
  • Yuanni Wang and
  • Wei Wu

30 March 2020

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...

  • Article
  • Open Access
102 Citations
7,357 Views
24 Pages

Binary Whale Optimization Algorithm for Dimensionality Reduction

  • Abdelazim G. Hussien,
  • Diego Oliva,
  • Essam H. Houssein,
  • Angel A. Juan and
  • Xu Yu

17 October 2020

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...

  • Article
  • Open Access
16 Citations
3,084 Views
25 Pages

7 August 2020

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...

  • Article
  • Open Access
4 Citations
1,798 Views
19 Pages

Two-Stage Dimensionality Reduction for Social Media Engagement Classification

  • Jose Luis Vieira Sobrinho,
  • Flavio Henrique Teles Vieira and
  • Alisson Assis Cardoso

3 February 2024

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...

  • Article
  • Open Access
3 Citations
5,105 Views
14 Pages

4 October 2019

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...

  • Article
  • Open Access
1,560 Views
19 Pages

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...

  • Article
  • Open Access
2,218 Views
18 Pages

7 September 2022

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...

  • Letter
  • Open Access
11 Citations
2,622 Views
10 Pages

Feature Extraction for Dimensionality Reduction in Cellular Networks Performance Analysis

  • Isabel de-la-Bandera,
  • David Palacios,
  • Jessica Mendoza and
  • Raquel Barco

4 December 2020

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...

  • Article
  • Open Access
1,968 Views
18 Pages

30 April 2021

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...

  • Proceeding Paper
  • Open Access
6 Citations
2,336 Views
6 Pages

Comparison and Evaluation of Dimensionality Reduction Techniques for Hyperspectral Data Analysis

  • K Nivedita Priyadarshini,
  • V Sivashankari,
  • Sulochana Shekhar and
  • K Balasubramani

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...

  • Article
  • Open Access
1,843 Views
14 Pages

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...

  • Article
  • Open Access
7 Citations
4,517 Views
18 Pages

Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data

  • Burkhard Hoppenstedt,
  • Manfred Reichert,
  • Klaus Kammerer,
  • Thomas Probst,
  • Winfried Schlee,
  • Myra Spiliopoulou and
  • Rüdiger Pryss

10 September 2019

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...

  • Article
  • Open Access
9 Citations
2,777 Views
16 Pages

Dimensionality Reduction for Smart IoT Sensors

  • Jorge Vizárraga,
  • Roberto Casas,
  • Álvaro Marco and
  • J. David Buldain

1 December 2020

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...

  • Article
  • Open Access
3 Citations
3,166 Views
18 Pages

15 November 2019

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...

  • Article
  • Open Access
17 Citations
4,951 Views
18 Pages

Consciousness and the Dimensionality of DOC Patients via the Generalized Ising Model

  • Pubuditha M. Abeyasinghe,
  • Marco Aiello,
  • Emily S. Nichols,
  • Carlo Cavaliere,
  • Salvatore Fiorenza,
  • Orsola Masotta,
  • Pasquale Borrelli,
  • Adrian M. Owen,
  • Anna Estraneo and
  • Andrea Soddu

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...

  • Article
  • Open Access
3 Citations
2,984 Views
25 Pages

Semi-Supervised Multi-Label Dimensionality Reduction Learning by Instance and Label Correlations

  • Runxin Li,
  • Jiaxing Du,
  • Jiaman Ding,
  • Lianyin Jia,
  • Yinong Chen and
  • Zhenhong Shang

3 February 2023

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...

  • Article
  • Open Access
11 Citations
5,075 Views
13 Pages

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...

  • Feature Paper
  • Review
  • Open Access
5 Citations
2,825 Views
19 Pages

22 April 2021

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...

  • Article
  • Open Access
154 Citations
7,164 Views
23 Pages

Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

  • Fulin Luo,
  • Hong Huang,
  • Yule Duan,
  • Jiamin Liu and
  • Yinghua Liao

1 August 2017

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...

  • Article
  • Open Access
1 Citations
3,549 Views
17 Pages

30 April 2025

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...

  • Feature Paper
  • Article
  • Open Access
11 Citations
4,113 Views
25 Pages

Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity

  • Alexander D. Kalian,
  • Emilio Benfenati,
  • Olivia J. Osborne,
  • David Gott,
  • Claire Potter,
  • Jean-Lou C. M. Dorne,
  • Miao Guo and
  • Christer Hogstrand

30 June 2023

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...

  • Article
  • Open Access
1 Citations
3,785 Views
14 Pages

Analyzing the Dimensionality of O*NET Cognitive Ability Ratings to Inform Assessment Design

  • Stephen G. Sireci,
  • Brendan Longe,
  • Javier Suárez-Álvarez and
  • Maria Elena Oliveri

1 November 2024

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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