Results 31 to 40 of about 7,354 (203)
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval [PDF]
, 2014 We present a simple but powerful reinterpretation of kernelized locality-sensitive hashing (KLSH), a general and popular method developed in the vision community for performing approximate nearest-neighbor searches in an arbitrary reproducing kernel ...
Jiang, Ke, Kulis, Brian, Que, Qichao
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Robust Kernel Principal Component Analysis With ℓ2,1-Regularized Loss Minimization
IEEE Access, 2020 Principal component analysis (PCA) is a widely used unsupervised method for dimensionality reduction. The kernelized version is called kernel principal component analysis (KPCA), which can capture the nonlinear data structure.
Duo Wang, Toshihisa Tanaka
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International Journal of Computational Intelligence Systems, 2021 Diagnosing incipient faults of rotating machines is very important for reducing economic losses and avoiding accidents caused by faults. However, diagnoses of locations and sizes of incipient faults are very difficult in a noisy background. In this paper,
Heli Wang +3 more
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Age Sensitivity of Face Recognition Algorithms [PDF]
, 2013 This paper investigates the performance degradation of facial recognition systems due to the influence of age. A comparative analysis of verification performance is conducted for four subspace projection techniques combined with four different distance ...
Deravi, Farzin +2 more
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Application of kernel principal component analysis for optical vector atomic magnetometry
Machine Learning: Science and Technology, 2023 Vector atomic magnetometers that incorporate electromagnetically induced transparency (EIT) allow for precision measurements of magnetic fields that are sensitive to the directionality of the observed field by virtue of fundamental physics.
James A McKelvy +8 more
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Clustering via kernel decomposition [PDF]
, 2006 Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this letter, the affinity matrix is created from the elements of a nonparametric density estimator and then decomposed to obtain ...
Girolami, M. +2 more
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Materials & Design, 2020 Despite outstanding and unique properties, the structure-property relationship of high entropy alloys (HEAs) is not well established. The machine learning (ML) is used to scrutinize the effect of nine physical quantities on four phases.
Lei Zhang +7 more
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IEEE Access, 2021 Quantitative structure-activity relationship (QSAR) modeling is an established approach for drug discovery, but many QSAR datasets suffer from the curse of dimensionality, a challenge that is usually addressed by using dimensionality reduction techniques
Shrooq A. Alsenan +2 more
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Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization [PDF]
, 2011 Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics.
Giannakis, Georgios B., Mateos, Gonzalo
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Ages and Masses of 0.64 million Red Giant Branch stars from the LAMOST Galactic Spectroscopic Survey
, 2019 We present a catalog of stellar age and mass estimates for a sample of 640\,986 red giant branch (RGB) stars of the Galactic disk from the LAMOST Galactic Spectroscopic Survey (DR4).
Bi, S. +16 more
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