NSTL国家科技图书文献中心

1. Fast and Understandable Nonlinear Supervised Dimensionality Reduction NSTL国家科技图书文献中心

Anri Patron | Rafael Savvides ... - 《Discovery Science,Part I》 - International Conference on Discovery Science - 2025, - 385～400 - 共16页

摘要： creation and dimensionality* reduction are essential tasks* | unsupervised dimensionality* reduction methods such as* | dimensionality reduction methods, such as canonical correlation | dimensionality reduction method, called Gradient Boosting | . Still, typically, the dimensionality* of these*

关键词： Supervised dimensionality reduction | Embedding | Boosting | Feature engineering

2. Fusion of data dimensionality reduction algorithms baced on category representation theory NSTL国家科技图书文献中心

Xiaoxiang Xu | Fanzhang Li ... - 《Fourth International Conference on Computer Vision,Application,and Algorithm (CVAA 2024)》 - International Conference on Computer Vision,Application,and Algorithm - 2025, - 1348633.1～1348633.10 - 共10页

摘要： representation for data dimensionality* reduction and provide a* | data dimensionality* reduction fusion representation* | proposed a data dimensionality* reduction fusion* | study the fusion representation of data dimensionality | reduction. We propose the basic concept of category

关键词： Category representation | Data dimensionality reduction | Data dimensionality reduction fusion representation

3. Local Information in Global Optimization with Dimensionality Reduction Schemes NSTL国家科技图书文献中心

Dmitri E. Kvasov | Vladimir A. Grishagi... - 《Numerical Computations,Part I》 - International Conference on Numerical Computations: Theory and Algorithms - 2025, - 336～342 - 共7页

摘要： based on the dimensionality* reduction approach* | framework of the dimensionality* reduction schemes.* | Numerical methods for continuous global | optimization are investigated in this contribution. They are | consisting in the extension of one-dimensional methods to

关键词： Lipschitz global optimization | Dimensionality reduction | Local information | Acceleration of convergence

4. Deep Ensemble Transformers for Dimensionality Reduction NSTL国家科技图书文献中心

Maria Nareklishvili | Marius Geitle - 《IEEE transactions on neural networks and learning systems》 - 2025,36(2) - 2091～2102 - 共12页

摘要：), a fast, scalable approach for dimensionality | reduction problems. This method leverages the power of | We propose deep ensemble transformers (DETs | deep neural networks and employs cascade ensemble | techniques as its fundamental feature extraction tool. To

关键词： Transformers | Random forests | Ensemble learning | Feature extraction | Neural networks | Artificial neural networks | Dimensionality reduction

5. Scatterplot selection for dimensionality reduction in multidimensional data visualization NSTL国家科技图书文献中心

Kaya Okada | Takayuki Itoh - 《Journal of visualization》 - 2025,28(1) - 205～221 - 共17页

摘要：Dimensionality reduction (DR) techniques for | multidimensional data serve as powerful tools for visualization | and understanding of the structure of the data | . Various DR methods have been developed to extract | specific features of the data over the years. However

关键词： Dimensionality reduction | Multidimensional data visualization | Scatterplot | Scatterplot selection | Evaluation of dimensionality reduction | Text data visualization

6. Entropic Semi-Supervised Dimensionality Reduction for Distance Metric Learning NSTL国家科技图书文献中心

Levada, Alexandre L.... - 《International journal of uncertainty, fuzziness and knowledge-based systems》 - 2025,33(2) - 219～234 - 共16页

摘要： dimensionality reduction are intrinsically related, since they | -Supervised Dimensionality* Reduction (SSDR) algorithm that* | Distance metric learning and nonlinear | are both different perspectives of the same | fundamental problem: to learn compact and meaningful data

关键词： Dimensionality reduction | Semi supervised learning | Statistical divergences | Metric learning

7. Low-correlation multilinear dimensionality reduction applied to volcano-seismic classification NSTL国家科技图书文献中心

Peixoto, Antonio Aug... | Fernandes, Carlos Al...... - 《Pattern Recognition》 - 2025,158 - 共12页

摘要：-Correlation Multilinear Dimensionality* Reduction (LC-MDR* | the dimensionality* of tensor data, called Low* | In the present work, a technique for reducing | ), is proposed. The method optimizes a cost function | that takes into account the data correlation

关键词： Dimensionality reduction | Tensor decompositions | Correlation | Classification | Seismic event

8. Quick Unsupervised Hyperspectral Dimensionality Reduction for Earth Observation: A Comparison NSTL国家科技图书文献中心

Daniela Lupu | Joseph L. Garrett ... - 《IEEE transactions on computational imaging》 - 2025,11 - 520～535 - 共16页

摘要：Dimensionality reduction can be applied to | dimensionality reduction often provide the basis for more | unsupervised dimensionality* reduction algorithms are tested* | dimensionality reduction and give guidance regarding how to | hyperspectral images so that the most useful data can be

关键词： Principal component analysis | Dimensionality reduction | Classification algorithms | Hyperspectral imaging | Object detection | Covariance matrices | Matrix decomposition | Image reconstruction | Accuracy | Pipelines

9. Dimensionality Reduction with Proper Symplectic Decomposition for Learning Hamiltonian Dynamics NSTL国家科技图书文献中心

Janis Bajars - 《Numerical Computations,Part II》 - International Conference on Numerical Computations: Theory and Algorithms - 2025, - 3～18 - 共16页

摘要： propose dimensionality* reduction with the proper* | orthogonal decomposition (POD) dimensionality-reduced | Structure-preserving deep learning has | recently received high attention, e.g., the development | of the symplecticity-preserving neural networks

关键词： Hamiltonian dynamics | Structure-preserving neural networks | Symplectic dimensionality reduction | Learning wave solutions

10. ITRMD: A Dimensionality Reduction Framework for Accurate and Efficient Multivariate KPI Anomaly Detection NSTL国家科技图书文献中心

Tianrun Gao | Decheng Zuo ... - 《Advanced Data Mining and Applications,Part IV》 - International Conference on Advanced Data Mining and Applications - 2025, - 327～341 - 共15页

摘要：. Therefore, dimensionality* reduction is important in MAD* | dimensionality reduction framework ITRMD, which reduces | is called the "Curse of dimensionality", which | . However, there are few researches on dimensionality | reduction methods specifically designed for MAD. In this

关键词： Supervised dimensionality reduction | Embedding | Boosting | Feature engineering

摘要： representation for data dimensionality* reduction and provide a* | data dimensionality* reduction fusion representation* | proposed a data dimensionality* reduction fusion* | study the fusion representation of data dimensionality | reduction. We propose the basic concept of category

关键词： Category representation | Data dimensionality reduction | Data dimensionality reduction fusion representation

摘要： based on the dimensionality* reduction approach* | framework of the dimensionality* reduction schemes.* | Numerical methods for continuous global | optimization are investigated in this contribution. They are | consisting in the extension of one-dimensional methods to

关键词： Lipschitz global optimization | Dimensionality reduction | Local information | Acceleration of convergence

摘要：), a fast, scalable approach for dimensionality | reduction problems. This method leverages the power of | We propose deep ensemble transformers (DETs | deep neural networks and employs cascade ensemble | techniques as its fundamental feature extraction tool. To

关键词： Transformers | Random forests | Ensemble learning | Feature extraction | Neural networks | Artificial neural networks | Dimensionality reduction

摘要：Dimensionality reduction (DR) techniques for | multidimensional data serve as powerful tools for visualization | and understanding of the structure of the data | . Various DR methods have been developed to extract | specific features of the data over the years. However

关键词： Dimensionality reduction | Multidimensional data visualization | Scatterplot | Scatterplot selection | Evaluation of dimensionality reduction | Text data visualization

摘要： dimensionality reduction are intrinsically related, since they | -Supervised Dimensionality* Reduction (SSDR) algorithm that* | Distance metric learning and nonlinear | are both different perspectives of the same | fundamental problem: to learn compact and meaningful data

关键词： Dimensionality reduction | Semi supervised learning | Statistical divergences | Metric learning

摘要：-Correlation Multilinear Dimensionality* Reduction (LC-MDR* | the dimensionality* of tensor data, called Low* | In the present work, a technique for reducing | ), is proposed. The method optimizes a cost function | that takes into account the data correlation

关键词： Dimensionality reduction | Tensor decompositions | Correlation | Classification | Seismic event

摘要：Dimensionality reduction can be applied to | dimensionality reduction often provide the basis for more | unsupervised dimensionality* reduction algorithms are tested* | dimensionality reduction and give guidance regarding how to | hyperspectral images so that the most useful data can be

关键词： Principal component analysis | Dimensionality reduction | Classification algorithms | Hyperspectral imaging | Object detection | Covariance matrices | Matrix decomposition | Image reconstruction | Accuracy | Pipelines

摘要： propose dimensionality* reduction with the proper* | orthogonal decomposition (POD) dimensionality-reduced | Structure-preserving deep learning has | recently received high attention, e.g., the development | of the symplecticity-preserving neural networks

关键词： Hamiltonian dynamics | Structure-preserving neural networks | Symplectic dimensionality reduction | Learning wave solutions

摘要：. Therefore, dimensionality* reduction is important in MAD* | dimensionality reduction framework ITRMD, which reduces | is called the "Curse of dimensionality", which | . However, there are few researches on dimensionality | reduction methods specifically designed for MAD. In this

关键词： KPI | Time-series | Multivariate KPI anomaly detection | Dimensionality reduction | Outlier

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： creation and dimensionality reduction are essential tasks | unsupervised dimensionality reduction methods such as | dimensionality reduction methods, such as canonical correlation | dimensionality reduction method, called Gradient Boosting | . Still, typically, the dimensionality of these

关键词： Supervised dimensionality reduction | Embedding | Boosting | Feature engineering

摘要： representation for data dimensionality reduction and provide a | data dimensionality reduction fusion representation | proposed a data dimensionality reduction fusion | study the fusion representation of data dimensionality | reduction. We propose the basic concept of category

关键词： Category representation | Data dimensionality reduction | Data dimensionality reduction fusion representation

摘要： based on the dimensionality reduction approach | framework of the dimensionality reduction schemes. | Numerical methods for continuous global | optimization are investigated in this contribution. They are | consisting in the extension of one-dimensional methods to

关键词： Lipschitz global optimization | Dimensionality reduction | Local information | Acceleration of convergence

摘要：), a fast, scalable approach for dimensionality | reduction problems. This method leverages the power of | We propose deep ensemble transformers (DETs | deep neural networks and employs cascade ensemble | techniques as its fundamental feature extraction tool. To

关键词： Transformers | Random forests | Ensemble learning | Feature extraction | Neural networks | Artificial neural networks | Dimensionality reduction

摘要：Dimensionality reduction (DR) techniques for | multidimensional data serve as powerful tools for visualization | and understanding of the structure of the data | . Various DR methods have been developed to extract | specific features of the data over the years. However

关键词： Dimensionality reduction | Multidimensional data visualization | Scatterplot | Scatterplot selection | Evaluation of dimensionality reduction | Text data visualization

摘要： dimensionality reduction are intrinsically related, since they | -Supervised Dimensionality Reduction (SSDR) algorithm that | Distance metric learning and nonlinear | are both different perspectives of the same | fundamental problem: to learn compact and meaningful data

关键词： Dimensionality reduction | Semi supervised learning | Statistical divergences | Metric learning

摘要：-Correlation Multilinear Dimensionality Reduction (LC-MDR | the dimensionality of tensor data, called Low | In the present work, a technique for reducing | ), is proposed. The method optimizes a cost function | that takes into account the data correlation

关键词： Dimensionality reduction | Tensor decompositions | Correlation | Classification | Seismic event

摘要：Dimensionality reduction can be applied to | dimensionality reduction often provide the basis for more | unsupervised dimensionality reduction algorithms are tested | dimensionality reduction and give guidance regarding how to | hyperspectral images so that the most useful data can be

关键词： Principal component analysis | Dimensionality reduction | Classification algorithms | Hyperspectral imaging | Object detection | Covariance matrices | Matrix decomposition | Image reconstruction | Accuracy | Pipelines

摘要： propose dimensionality reduction with the proper | orthogonal decomposition (POD) dimensionality-reduced | Structure-preserving deep learning has | recently received high attention, e.g., the development | of the symplecticity-preserving neural networks

关键词： Hamiltonian dynamics | Structure-preserving neural networks | Symplectic dimensionality reduction | Learning wave solutions

摘要：. Therefore, dimensionality reduction is important in MAD | dimensionality reduction framework ITRMD, which reduces | is called the "Curse of dimensionality", which | . However, there are few researches on dimensionality | reduction methods specifically designed for MAD. In this

关键词： KPI | Time-series | Multivariate KPI anomaly detection | Dimensionality reduction | Outlier

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： representation for data dimensionality* reduction and provide a* | data dimensionality* reduction fusion representation* | proposed a data dimensionality* reduction fusion* | study the fusion representation of data dimensionality | reduction. We propose the basic concept of category

摘要： based on the dimensionality* reduction approach* | framework of the dimensionality* reduction schemes.* | Numerical methods for continuous global | optimization are investigated in this contribution. They are | consisting in the extension of one-dimensional methods to

摘要： dimensionality reduction are intrinsically related, since they | -Supervised Dimensionality* Reduction (SSDR) algorithm that* | Distance metric learning and nonlinear | are both different perspectives of the same | fundamental problem: to learn compact and meaningful data

摘要：-Correlation Multilinear Dimensionality* Reduction (LC-MDR* | the dimensionality* of tensor data, called Low* | In the present work, a technique for reducing | ), is proposed. The method optimizes a cost function | that takes into account the data correlation

摘要：Dimensionality reduction can be applied to | dimensionality reduction often provide the basis for more | unsupervised dimensionality* reduction algorithms are tested* | dimensionality reduction and give guidance regarding how to | hyperspectral images so that the most useful data can be

摘要： propose dimensionality* reduction with the proper* | orthogonal decomposition (POD) dimensionality-reduced | Structure-preserving deep learning has | recently received high attention, e.g., the development | of the symplecticity-preserving neural networks

摘要：. Therefore, dimensionality* reduction is important in MAD* | dimensionality reduction framework ITRMD, which reduces | is called the "Curse of dimensionality", which | . However, there are few researches on dimensionality | reduction methods specifically designed for MAD. In this