Results 21 to 30 of about 3,487,202 (255)
Dimensionality reduction by LPP‐L21
IET Computer Vision, 2018 Locality preserving projection (LPP) is one of the most representative linear manifold learning methods and well exploits intrinsic structure of data. However, the performance of LPP remarkably degenerate in the presence of outliers.
Shujian Wang +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 +3 more
core +1 more source
Dimensionality Reduction Data Sets
, 2021 Benchmark data sets from various sources that can be used to test and compare dimensionality reduction ...
Igor Matheus Souza Moreira +1 more
core +1 more source
On dimensionality reduction for client specific discriminant analysis with application to face verification [PDF]
, 2004 In this paper we propose a study on dimensionality reduction for client specific discriminant analysis with application to face verification. A new algorithm of face verification based on client specific discriminant analysis is developed. Two aspects of
Xiaojun Wu +11 more
core +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 +15 more
core +1 more source
Neighbors-Based Graph Construction for Dimensionality Reduction
IEEE Access, 2019 Dimensionality reduction is a fundamental task in the field of data mining and machine learning. In many scenes, examples in high-dimensional space usually lie on low-dimensional manifolds; thus, learning the low-dimensional embedding is important.
Hui Tian +3 more
doaj +1 more source
Proximities in dimensionality reduction
, 2022 International audienceDimensionality reduction aims at representing high-dimensional data in a lower-dimensional representation, while preserving their structure (clusters, outliers, manifold).
Journaux, Ludovic +4 more
core +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 +2 more
doaj +1 more source
A review of dimensionality reduction techniques for processing hyper-spectral optical signal [PDF]
, 2019 Hyper-spectral sensors take measurements in the narrow contiguous bands across the electromagnetic spectrum. Usually, the goal is to detect a certain object or a component of the medium with unique spectral signatures.
del Águila, Ana +2 more
core +1 more source
Binary Whale Optimization Algorithm for Dimensionality Reduction
Mathematics, 2020 Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good ...
Abdelazim G. Hussien +4 more
doaj +1 more source