NSTL国家科技图书文献中心

1. 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： data dimensionality reduction fusion representation | study the fusion representation of data dimensionality | representation for data dimensionality reduction and provide a | proposed a data dimensionality reduction fusion | representation algorithm based on a data dimensionality

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

2. Multi-Site Wireless Channel Charting Through Latent Space Alignment NSTL国家科技图书文献中心

Yamil Vindas | Maxime Guillaud - 《2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications》 - IEEE International Workshop on Signal Processing Advances in Wireless Communications - 2024, - 826～830 - 共5页

摘要： propagation. By applying dimensionality* reduction to channel* | classical dimensionality* reduction metrics using measured* | -dimensional representation* of the channel state, which is* | latent space, effectively performing the fusion* of* | data, as well as their degree of distributedness.

关键词： Wireless communication | Dimensionality reduction | Training | Measurement | Base stations | Focusing | Distributed databases | Signal processing | Data mining | Space stations

3. RGB-Infrared Image Fusion and Classification based on Tensor Decomposition NSTL国家科技图书文献中心

Ke Liu | Hang Li ... - 《Conference on Spectral Technology and Applications (CSTA 2024),Part Two of Two Parts》 - Conference on Spectral Technology and Applications - 2024, - 共9页

摘要：, dimensionality* reduction, target detection, spectral unmixing* | (RGB) and infrared (IR) data* fusion and* | data source. Consequently, remote sensing images | from multimodal data* has facilitated a more* | representation methods, and multi-scale, multi-feature

关键词： Remote sensing image | Multimodal fusion | Tensor decomposition | Object classification

4. Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning NSTL国家科技图书文献中心

Ming Cheng | Ziyi Zhou ... - 《2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops》 - IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops - 2024, - 2546～2555 - 共10页

摘要： construction, marking a leap in dimensionality reduction | In the landscape of spatio-temporal data | analytics, effective trajectory representation* learning is* | transformative graph modeling and representation* learning of* | neighbors (KNN) algorithm with feature fusion* for graph*

关键词： Representation learning | Dimensionality reduction | Accuracy | Geology | Pipelines | Nearest neighbor methods | Feature extraction

5. 3D seismic mask auto encoder: Seismic inversion using transformer-based reconstruction representation learning NSTL国家科技图书文献中心

Dou Y. | Li K. - 《Computers and geotechnics》 - 2024,169(May) - 1.1～1.14 - 共14页 - 被引量：1

摘要：) Aggregated dimensionality* reduction encoding to handle* | impedance from seismic data* is a crucial step in reservoir* | characterization. While data-driven impedance inversion based on | amounts of unlabeled data* can be transferred to* | downstream tasks with minimal labeled data. However

关键词： Pretrained foundation models (PFMs) | Representation learning | Seismic data | Seismic inversion | Self-supervised learning

6. Highly compressed image representation for classification and content retrieval NSTL国家科技图书文献中心

Lazewski, Stanislaw | Cyganek, Boguslaw - 《Integrated Computer-Aided Engineering》 - 2024,31(3) - 267～284 - 共18页

摘要：, which leads to a significant reduction* in* | dimensionality. Further on, by applying a floating-point | obtained by simple output fusion* of ResNet-50. As a* | result, the representation* of a single image from the* | fusion of the last ResNet-50 residual block, we achieve

关键词： Image classification | content-based image recognition (CBIR) | deep semantic features | PCA-ResFeats | ResNet-50 | POINT | NETWORK | SCALE

7. A journey from univariate to multivariate functional time series: A comprehensive review NSTL国家科技图书文献中心

Hossein Haghbin | Mehdi Maadooliat - 《Wiley interdisciplinary reviews. Computational statistics》 - 2024,16(1) - n/a～n/a - 共22页

摘要： explore strategies for dimensionality reduction | forecasting time‐dependent data* with functional attributes* | into representation, estimation, and modeling | the intricacies posed by high‐dimensional data. We | refinement and innovation. Through a fusion* of multivariate*

关键词： forecasting | functional principal component analysis | functional time series | multivariate functional time series

8. Heterogeneous Cuckoo Search-Based Unsupervised Band Selection for Hyperspectral Image Classification NSTL国家科技图书文献中心

Meng Wu | Xianfeng Ou ... - 《IEEE Transactions on Geoscience and Remote Sensing》 - 2024,62 - 1～16 - 共16页 - 被引量：1

摘要： relevant features from such high-dimensional data. Band | selection (BS), one of the most fundamental dimensionality | reduction (DR) techniques, removes redundant bands while | self-representation* (MGSR), neighborhood grouping* | graph fusion* (RMGF).*

关键词： Matched filters | Hyperspectral imaging | Classification algorithms | Support vector machines | Optimization | Dimensionality reduction | Correlation

9. MOVNG: Applied a Novel Sparse Fusion Representation into GTCN for Pan-Cancer Classification and Biomarker Identification NSTL国家科技图书文献中心

Xin Chen | Yun Tie ... - 《Advanced Intelligent Computing Technology and Applications: 19th International Conference, ICIC 2023, Zhengzhou, China, August 10-13, 2023, Proceedings, Part I》 - International Conference on Intelligent Computing - 2023, - 604～615 - 共12页

摘要： dimensionality, and small sample sizes of this data pose | representation. Dimension reduction is also important to avoid | samples in omics data* fusion. Thus, we proposed a novel* | sparse fusion* representation method based on VAE-NN* | inter- and intra-omics data* fusion in high-level*

关键词： Multi-omics data | Pan-cancer classification | Biomarker identification | Graph tree convolution networks | Spare fusion representation

10. Dimensionality Reduction Visualization Analysis of Financial Data based on Semantic Feature Group NSTL国家科技图书文献中心

Ke Wang | Menghua Luo ... - 《Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), Part One of Two Parts: 16-18 December 2022.Shenyang, China》 - International Conference on Machine Learning and Computer Application - 2023, - 126362J.1～126362J.7 - 共7页

摘要： verified by comparing the data dimensionality reduction | , dimensionality* reduction algorithms and principal component* | With the continuous development of data | science and financial technology, financial data | in the field of financial data* analysis today. From*

摘要： data dimensionality reduction fusion representation | study the fusion representation of data dimensionality | representation for data dimensionality reduction and provide a | proposed a data dimensionality reduction fusion | representation algorithm based on a data dimensionality

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

关键词： Wireless communication | Dimensionality reduction | Training | Measurement | Base stations | Focusing | Distributed databases | Signal processing | Data mining | Space stations

摘要：, dimensionality* reduction, target detection, spectral unmixing* | (RGB) and infrared (IR) data* fusion and* | data source. Consequently, remote sensing images | from multimodal data* has facilitated a more* | representation methods, and multi-scale, multi-feature

关键词： Remote sensing image | Multimodal fusion | Tensor decomposition | Object classification

摘要： construction, marking a leap in dimensionality reduction | In the landscape of spatio-temporal data | analytics, effective trajectory representation* learning is* | transformative graph modeling and representation* learning of* | neighbors (KNN) algorithm with feature fusion* for graph*

关键词： Representation learning | Dimensionality reduction | Accuracy | Geology | Pipelines | Nearest neighbor methods | Feature extraction

关键词： Pretrained foundation models (PFMs) | Representation learning | Seismic data | Seismic inversion | Self-supervised learning

摘要：, which leads to a significant reduction* in* | dimensionality. Further on, by applying a floating-point | obtained by simple output fusion* of ResNet-50. As a* | result, the representation* of a single image from the* | fusion of the last ResNet-50 residual block, we achieve

关键词： Image classification | content-based image recognition (CBIR) | deep semantic features | PCA-ResFeats | ResNet-50 | POINT | NETWORK | SCALE

摘要： explore strategies for dimensionality reduction | forecasting time‐dependent data* with functional attributes* | into representation, estimation, and modeling | the intricacies posed by high‐dimensional data. We | refinement and innovation. Through a fusion* of multivariate*

关键词： forecasting | functional principal component analysis | functional time series | multivariate functional time series

摘要： relevant features from such high-dimensional data. Band | selection (BS), one of the most fundamental dimensionality | reduction (DR) techniques, removes redundant bands while | self-representation* (MGSR), neighborhood grouping* | graph fusion* (RMGF).*

关键词： Matched filters | Hyperspectral imaging | Classification algorithms | Support vector machines | Optimization | Dimensionality reduction | Correlation

摘要： dimensionality, and small sample sizes of this data pose | representation. Dimension reduction is also important to avoid | samples in omics data* fusion. Thus, we proposed a novel* | sparse fusion* representation method based on VAE-NN* | inter- and intra-omics data* fusion in high-level*

关键词： Multi-omics data | Pan-cancer classification | Biomarker identification | Graph tree convolution networks | Spare fusion representation

摘要： verified by comparing the data dimensionality reduction | , dimensionality* reduction algorithms and principal component* | With the continuous development of data | science and financial technology, financial data | in the field of financial data* analysis today. From*

关键词： Semantic feature group | Visual analytics | Dimensionality Reduction Visualization | Financial Data Visualization | Credit Card Data Analysis

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： data dimensionality reduction fusion representation | study the fusion representation of data dimensionality | representation for data dimensionality reduction and provide a | proposed a data dimensionality reduction fusion | representation algorithm based on a data dimensionality

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

摘要： propagation. By applying dimensionality reduction to channel | classical dimensionality reduction metrics using measured | -dimensional representation of the channel state, which is | latent space, effectively performing the fusion of | data, as well as their degree of distributedness.

关键词： Wireless communication | Dimensionality reduction | Training | Measurement | Base stations | Focusing | Distributed databases | Signal processing | Data mining | Space stations

摘要：, dimensionality reduction, target detection, spectral unmixing | (RGB) and infrared (IR) data fusion and | data source. Consequently, remote sensing images | from multimodal data has facilitated a more | representation methods, and multi-scale, multi-feature

关键词： Remote sensing image | Multimodal fusion | Tensor decomposition | Object classification

摘要： construction, marking a leap in dimensionality reduction | In the landscape of spatio-temporal data | analytics, effective trajectory representation learning is | transformative graph modeling and representation learning of | neighbors (KNN) algorithm with feature fusion for graph

关键词： Representation learning | Dimensionality reduction | Accuracy | Geology | Pipelines | Nearest neighbor methods | Feature extraction

摘要：) Aggregated dimensionality reduction encoding to handle | impedance from seismic data is a crucial step in reservoir | characterization. While data-driven impedance inversion based on | amounts of unlabeled data can be transferred to | downstream tasks with minimal labeled data. However

关键词： Pretrained foundation models (PFMs) | Representation learning | Seismic data | Seismic inversion | Self-supervised learning

摘要：, which leads to a significant reduction in | dimensionality. Further on, by applying a floating-point | obtained by simple output fusion of ResNet-50. As a | result, the representation of a single image from the | fusion of the last ResNet-50 residual block, we achieve

关键词： Image classification | content-based image recognition (CBIR) | deep semantic features | PCA-ResFeats | ResNet-50 | POINT | NETWORK | SCALE

摘要： explore strategies for dimensionality reduction | forecasting time‐dependent data with functional attributes | into representation, estimation, and modeling | the intricacies posed by high‐dimensional data. We | refinement and innovation. Through a fusion of multivariate

关键词： forecasting | functional principal component analysis | functional time series | multivariate functional time series

摘要： relevant features from such high-dimensional data. Band | selection (BS), one of the most fundamental dimensionality | reduction (DR) techniques, removes redundant bands while | self-representation (MGSR), neighborhood grouping | graph fusion (RMGF).

关键词： Matched filters | Hyperspectral imaging | Classification algorithms | Support vector machines | Optimization | Dimensionality reduction | Correlation

摘要： dimensionality, and small sample sizes of this data pose | representation. Dimension reduction is also important to avoid | samples in omics data fusion. Thus, we proposed a novel | sparse fusion representation method based on VAE-NN | inter- and intra-omics data fusion in high-level

关键词： Multi-omics data | Pan-cancer classification | Biomarker identification | Graph tree convolution networks | Spare fusion representation

摘要： verified by comparing the data dimensionality reduction | , dimensionality reduction algorithms and principal component | With the continuous development of data | science and financial technology, financial data | in the field of financial data analysis today. From

关键词： Semantic feature group | Visual analytics | Dimensionality Reduction Visualization | Financial Data Visualization | Credit Card Data Analysis

4008-161-200 800-990-8900

国家科技图书文献中心

摘要：, dimensionality* reduction, target detection, spectral unmixing* | (RGB) and infrared (IR) data* fusion and* | data source. Consequently, remote sensing images | from multimodal data* has facilitated a more* | representation methods, and multi-scale, multi-feature

摘要： construction, marking a leap in dimensionality reduction | In the landscape of spatio-temporal data | analytics, effective trajectory representation* learning is* | transformative graph modeling and representation* learning of* | neighbors (KNN) algorithm with feature fusion* for graph*

摘要：, which leads to a significant reduction* in* | dimensionality. Further on, by applying a floating-point | obtained by simple output fusion* of ResNet-50. As a* | result, the representation* of a single image from the* | fusion of the last ResNet-50 residual block, we achieve

摘要： explore strategies for dimensionality reduction | forecasting time‐dependent data* with functional attributes* | into representation, estimation, and modeling | the intricacies posed by high‐dimensional data. We | refinement and innovation. Through a fusion* of multivariate*

摘要： relevant features from such high-dimensional data. Band | selection (BS), one of the most fundamental dimensionality | reduction (DR) techniques, removes redundant bands while | self-representation* (MGSR), neighborhood grouping* | graph fusion* (RMGF).*

摘要： verified by comparing the data dimensionality reduction | , dimensionality* reduction algorithms and principal component* | With the continuous development of data | science and financial technology, financial data | in the field of financial data* analysis today. From*