Results 31 to 40 of about 4,379,261 (288)

Nonlinear Dynamic Field Embedding: On Hyperspectral Scene Visualization [PDF]

, 2012
In many areas of research, complex signals are commonly represented by high dimensional feature vectors. However, high dimensional vectors are difficult to analyze and interpret due to the curse of dimensionality.
Lunga, Dalton, Erosy, Okan
core   +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

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

NON-LINEAR AUTOENCODER BASED ALGORITHM FOR DIMENSIONALITY REDUCTION OF AIRBORNE HYPERSPECTRAL DATA [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
Hyperspectral remote sensing is an advanced remote sensing technology that enhances the ability of accurate classification due to presence of narrow contiguous bands.
S. Priya, R. Ghosh, B. K. Bhattacharya
doaj   +1 more source

Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex

eLife, 2021
Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only
Eric Kenji Lee, Hymavathy Balasubramanian, Alexandra Tsolias, Stephanie Udochukwu Anakwe, Maria Medalla, Krishna V Shenoy, Chandramouli Chandrasekaran   +6 more
doaj   +1 more source

Foundations of Coupled Nonlinear Dimensionality Reduction

CoRR, 2015
In this paper we introduce and analyze the learning scenario of \emph{coupled nonlinear dimensionality reduction}, which combines two major steps of machine learning pipeline: projection onto a manifold and subsequent supervised learning. First, we present new generalization bounds for this scenario and, second, we introduce an algorithm that follows ...
Dmitry Storcheus, Mehryar Mohri, Afshin Rostamizadeh   +2 more
openaire   +3 more sources

Methods for Measurement and Analysis of Full Hand Angular Kinematics Using Electromagnetic Tracking Sensors

IEEE Access, 2022
The human hand needs a large number of sensors to measure kinematics owing to its large number of degrees of freedom. Existing devices like data gloves and optical trackers are associated with calibration, line of sight, and accuracy problems.
Prajwal Shenoy, Vignesh Sompur, Varadhan Skm   +2 more
doaj   +1 more source

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

Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction

, 2012
Gisbrecht A, Mokbel B, Hammer B. Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction.
Hammer, Barbara ; https://orcid.org/, B. Mokbel, Mokbel, B., B. Hammer, Hammer, B., Gisbrecht, A., Gisbrecht, Andrej, A. Gisbrecht, Mokbel, Bassam   +8 more
core   +1 more source

The impact of leukocyte‐associated RNA retention on platelet RNA‐based cancer detection

Molecular Oncology, EarlyView.
Platelets carry RNA signals that may help detect cancer, but platelet samples can also retain RNA from white blood cells. We found that this retained white blood cell signal did not significantly affect machine learning cancer classification, suggesting that the cancer‐related RNA signal in platelets is largely independent of leukocyte‐associated RNA ...
Michał Sieczczyński, Krzysztof Pastuszak, Anna Samelak‐Czajka, Zuzanna Cemka, Paulina Jackowiak, Tomasz Stefaniak, Sylwia Łapińska‐Szumczyk, Jacek Jassem, Anna J. Żaczek, Matthew T. Rondina, Myron G. Best, Sjors G. J. G. In 't Veld, Anna Supernat   +12 more
wiley   +1 more source