Results 291 to 300 of about 8,013,557 (321)

Dimensionality Reduction With Enhanced Hybrid-Graph Discriminant Learning for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing, 2020
Dimensionality reduction (DR) is an important way of improving the classification accuracy of a hyperspectral image (HSI). Graph learning, which can effectively reveal the intrinsic relationships of data, has been widely used in the case of HSIs. However,
Fulin Luo, Liangpei Zhang, Bo Du, Lefei Zhang   +3 more
semanticscholar   +1 more source

Joint Principal Component and Discriminant Analysis for Dimensionality Reduction

IEEE Transactions on Neural Networks and Learning Systems, 2020
Linear discriminant analysis (LDA) is the most widely used supervised dimensionality reduction approach. After removing the null space of the total scatter matrix $S_{t}$ via principal component analysis (PCA), the LDA algorithm can avoid the small ...
Xiaowei Zhao, Jun Guo, F. Nie, Ling Chen, Zhihui Li, Huaxiang Zhang   +5 more
semanticscholar   +1 more source

Dimensionality Reduction

2023
Frederic Ros, Rabia Riad
  +5 more sources

Distributed Dimensionality Reduction Fusion Estimation for Cyber-Physical Systems Under DoS Attacks

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019
This paper studies the distributed dimensionality reduction fusion estimation problem for a class of cyber-physical systems (CPSs) under denial-of-service (DoS) attacks.
Bo Chen, D. Ho, Wen-an Zhang, Li Yu
semanticscholar   +1 more source

A New Formulation of Linear Discriminant Analysis for Robust Dimensionality Reduction

IEEE Transactions on Knowledge and Data Engineering, 2019
Dimensionality reduction is a critical technology in the domain of pattern recognition, and linear discriminant analysis (LDA) is one of the most popular supervised dimensionality reduction methods.
Haifeng Zhao, Z. Wang, F. Nie
semanticscholar   +1 more source

The Impact of Data Pre-Processing Techniques and Dimensionality Reduction on the Accuracy of Machine Learning

2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON), 2019
Data pre-processing is considered as the core stage in machine learning and data mining. Normalization, discretization, and dimensionality reduction are well-known techniques in data pre-processing. This research paper seeks to examine the effects of Min-
Hadeel Obaid, Saad Ahmed Dheyab, Sana Sabah Sabry   +2 more
semanticscholar   +1 more source

Nonlinear dimensionality reduction by locally linear embedding.

Science, 2000
S. Roweis, L. Saul
semanticscholar   +1 more source

A global geometric framework for nonlinear dimensionality reduction.

Science, 2000
J. Tenenbaum, V. Silva, John C. Langford
semanticscholar   +1 more source

Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

Neural Computation, 2003
M. Belkin, P. Niyogi
semanticscholar   +1 more source

Dimensionality Reduction by Learning an Invariant Mapping

Computer Vision and Pattern Recognition, 2006
R. Hadsell, S. Chopra, Yann LeCun
semanticscholar   +1 more source