Results 351 to 360 of about 8,286,998 (397)

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

Dimensionality reduction and generalization

Proceedings of the 24th international conference on Machine learning, 2007
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning problems. We show that performing KPCA and then ordinary least squares on the projected data, a procedure known as kernel principal component regression (KPCR), is equivalent ...
MOSCI, SOFIA, ROSASCO, LORENZO, VERRI, ALESSANDRO   +2 more
openaire   +3 more sources

Dimensional reduction and symplectic reduction

Il Nuovo Cimento B Series 11, 1983
The method of dimensional reduction as applied to pure Yang-Mills theory by Manton and Harnadet al. can be related to the symplectic-reduction scheme developed by Marsden and Weinstein when applied to the phase space for a «classical» particle moving in the presence of a Yang-Mills field.
S. Shnider, S. Shnider, S. Sternberg, S. Sternberg   +3 more
openaire   +2 more sources

Dimensionality Reduction

2019
Dimensionality reduction is a hot research topic in data analysis today. Thanks to the advances in high-performance computing technologies and in the engineering eld, we entered in the so-called big-data era and an enormous quantity of data is available in every scientificc area, ranging from social networking, economy and politics to e-health and life
openaire   +2 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

Dimensionality Reduction

Encyclopedia of Machine Learning and Data Mining, 2018
Many problem classes in machine learning are inherently high dimensional. Natural language processing problems, for instance, often involve the extraction of meaning from words, which can appear in an intractably large number of potential sequences in ...
Michail Vlachos
semanticscholar   +1 more source

Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis

Structural Health Monitoring, 2018
One of the main challenges that the industry faces when dealing with massive data for failure diagnosis is high dimensionality of such data. This can be tackled by dimensionality reduction method such as principal components analysis, which usually ...
Gabriel San Martín, Enrique López Droguett, V. Meruane, Márcio das Chagas Moura   +3 more
semanticscholar   +1 more source

Metric Learning in Dimensionality Reduction

Proceedings of the International Conference on Pattern Recognition Applications and Methods, 2015
The emerging big dimensionality in digital domains causes the need of powerful non-linear dimensionality reduction techniques for a rapid and intuitive visual data access. While a couple of powerful non-linear dimensionality reduction tools have been proposed in the last years, their applicability is limited in practice: since a non-linear ...
Schulz, Alexander, Hammer, Barbara
openaire   +2 more sources