Results 21 to 30 of about 26,804 (294)

Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait [PDF]

, 2010
Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis.
Small, M, Zhang Jie, Jie Zhang, Zhang Kai, Zhang, Kai, Kai Zhang, Feng, J, Feng, Jianfeng, Jianfeng Feng, Small Michael, Zhang, Jie, Michael Small, Small, Michael, Zhang, K, Feng Jianfeng, Zhang, J   +15 more
core   +1 more source

Nonlinear Dimensionality Reduction Based on HSIC Maximization

IEEE Access, 2018
Hilbert-Schmidt independence criterion (HSIC) is typically used to measure the statistical dependence between two sets of data. HSIC first transforms these two sets of data into two reproducing Kernel Hilbert spaces (RKHS), respectively, and then ...
Zhengming Ma, Zengrong Zhan, Xiaoyuan Ouyang, Xue Su   +3 more
doaj   +1 more source

Local Feature Discriminant Projection [PDF]

, 2016
In this paper, we propose a novel subspace learning algorithm called Local Feature Discriminant Projection (LFDP) for supervised dimensionality reduction of local features.
Zhen, Xiantong, Shao, Ling, He, Xiaofei, Yu, Mengyang   +3 more
core   +1 more source

Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping

Mathematics, 2021
This work explores neural approximation for nonlinear dimensionality reduction mapping based on internal representations of graph-organized regular data supports.
Sheng-Shiung Wu, Sing-Jie Jong, Kai Hu, Jiann-Ming Wu   +3 more
doaj   +1 more source

IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees

Array, 2021
High-dimensional data with many features are usually challenging to represent with standard visualization techniques. Usually, one has to resort to dimensionality reduction techniques such as PCA, MDS or t-SNE to represent such data.
Adrien Bibal, Antoine Clarinval, Bruno Dumas, Benoît Frénay   +3 more
doaj   +1 more source

Improving Dimensionality Reduction Projections for Data Visualization

Applied Sciences, 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 aim ...
Bardia Rafieian, Pedro Hermosilla, Pere-Pau Vázquez   +2 more
doaj   +1 more source

Nonlinear Dimensionality Reduction for Face Recognition [PDF]

, 2009
Principal component analysis (PCA) has long been a dominating linear technique for dimensionality reduction. Many nonlinear methods and neural networks have been proposed to extend PCA for complex nonlinear data. They include kernel PCA, local linear embedding, isomap, self-organising map (SOM), and visualization induced SOM (ViSOM), a variant of SOM ...
Weilin Huang, Hujun Yin
openaire   +1 more source

Spline Embedding for Nonlinear Dimensionality Reduction [PDF]

, 2006
This paper presents a new algorithm for nonlinear dimensionality reduction (NLDR). Smoothing splines are used to map the locally-coordinatized data points into a single global coordinate system of lower dimensionality. In this work setting, we can achieve two goals.
Shiming Xiang, Feiping Nie 0001, Changshui Zhang, Chunxia Zhang 0001   +3 more
openaire   +1 more source

Application of Linear and Nonlinear Dimensionality Reduction Methods [PDF]

, 2012
Dimensionality reduction methods have proved to be important tools in exploratory analysis as well as confirmatory analysis for data mining in various fields of science and technology. Where ever applications involve reducing to fewer dimensions, feature
Sun, Mingui, Vinjamuri, Ramana, Wang, Wei, Mao, Zhi-Hong   +3 more
core   +1 more source

Semisupervised Kernel Marginal Fisher Analysis for Face Recognition

The Scientific World Journal, 2013
Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA ...
Ziqiang Wang, Xia Sun, Lijun Sun, Yuchun Huang   +3 more
doaj   +1 more source