Results 61 to 70 of about 29,752 (189)
Engineering ProceedingsIn this study, we delve into the dynamics of the Sri Lankan government bond market, building upon prior research that focused on the application of principal component analysis (PCA) in modelling sovereign yield curves.
K P N Sanjeewa Dayarathne +1 more
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PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification
, 2021 The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands.
MA Mamun (15370474) +2 more
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Kernel principal component analysis (KPCA) for the de-noising of communication signals [PDF]
, 2002 This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a ...
Koutsogiannis, G. +2 more
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Tensor Learning in Multi-view Kernel PCA [PDF]
, 2018 In many real-life applications data can be described through multiple representations, or views. Multi-view learning aims at combining the information from all views, in order to obtain a better performance. Most well-known multi-view methods optimize some form of correlation between two views, while in many applications there are three or more views ...
Houthuys, Lynn, Suykens, Johan A.K.
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On the Eigenspectrum of the Gram matrix and the generalisation error of kernel PCA
, 2004 In this paper we analyze the relationships between the eigenvalues of the m x m Gram matrix K for a kernel k(.,.) corresponding to a sample x1,...,xm drawn from a density p(x) and the eigenvalues of the corresponding continuous eigenproblem. We bound the
Shawe-Taylor, John +3 more
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Kernel PCA for Out-of-Distribution Detection
Advances in Neural Information Processing Systems 37Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs). Existing works have shown the insufficiency of Principal Component Analysis (PCA) straightforwardly applied on the features of DNNs in detecting OoD data from In-Distribution (InD) data.
Kun Fang 0004 +5 more
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Human gait recognition with 3-D wavelets and kernel based subspace projections [PDF]
Gait recognition can be regarded as a problem of uniquely representing spatiotemporal surfaces associated with a person?s walking pattern in an efficient manner.
Masood, K. +1 more
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Kernel PCA and the Nyström method [PDF]
, 2022 This thesis treats kernel PCA and the Nystrom method. We present a novel incre- ¨ mental algorithm for calculation of kernel PCA, which we extend to incremental calculation of the Nystrom approximation.
Hallgren, Fredrik
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Shock and VibrationTo detect internal valve leakage in liquid-filled pipelines, a method using kernel principal component analysis (KPCA) is proposed to analyze acoustic emission (AE) signals for leakage detection.
Runlin Zhang +4 more
doaj +1 more source
Two view learning: SVM-2K, theory and practice
, 2005 Kernel methods make it relatively easy to define complex highdimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task.
Hardoon, DR +11 more
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