Results 31 to 40 of about 408,145 (290)
Brent Crude Oil Daily Price Forecast by Combining Principal Components Analysis and Support Vector Regression methods [PDF]
Pizhūhishnāmah-i Iqtiṣād-i Inirzhī-i Īrān, 2017 Anticipating process of crude oil prices and its fluctuations volatility has always been one of the challenges the traders face in the exchange oil markets. This study estimates the Brent crude oil daily price forecast with a proposed hybrid model.
Elham Hajikaram, Roya Darabi
doaj +1 more source
Principal Flow Patterns across renewable electricity networks
, 2018 Using Principal Component Analysis (PCA), the nodal injection and line flow patterns in a network model of a future highly renewable European electricity system are investigated. It is shown that the number of principal components needed to describe 95$\%
Brown, Tom +5 more
core +1 more source
GrIP-PCA: Grassmann Iterative P-Norm Principal Component Analysis [PDF]
IEEE Open Journal of Signal Processing, 2020 Principal component analysis is one of the most commonly used methods for dimensionality reduction in signal processing. However, the most commonly used PCA formulation is based on the L2-norm, which can be highly influenced by outlier data. In recent years, there has been growing interest in the development of more robust PCA methods.
Breton Minnehan +2 more
openaire +2 more sources
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., Soraghan, J.J.
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Molecular Oncology, EarlyView.AZD9291 has shown promise in targeted cancer therapy but is limited by resistance. In this study, we employed metabolic labeling and LC–MS/MS to profile time‐resolved nascent protein perturbations, allowing dynamic tracking of drug‐responsive proteins. We demonstrated that increased NNMT expression is associated with drug resistance, highlighting NNMT ...
Zhanwu Hou +5 more
wiley +1 more source
Combination of DEA and PCA for Full Ranking of Decision Making Units [PDF]
مدیریت صنعتی, 2009 This paper presents a combination of Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) to reduce the dimensionality of data set. DEA is known as effective tool for assessment and benchmarking.
Mojtaba Khazaei, Hamid Reza Izadbakhsh
doaj
Memory efficient PCA methods for large group ICA
Frontiers in Neuroscience, 2016 Principal component analysis (PCA) is widely used for data reduction in group independent component analysis (ICA) of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components ...
Srinivas eRachakonda +6 more
doaj +1 more source
Parallel Analysis: a Method for Determining Significant Principal Components [PDF]
, 1995 Numerous ecological studies use Principal Components Analysis (PCA) for exploratory analysis and data reduction. Determination of the number of components to retain is the most crucial problem confronting the researcher when using PCA.
Fralish, James S +4 more
core +2 more sources
Molecular Oncology, EarlyView.This study indicates that Merkel cell carcinoma (MCC) does not originate from Merkel cells, and identifies gene, protein & cellular expression of immune‐linked and neuroendocrine markers in primary and metastatic Merkel cell carcinoma (MCC) tumor samples, linked to Merkel cell polyomavirus (MCPyV) status, with enrichment of B‐cell and other immune cell
Richie Jeremian +10 more
wiley +1 more source
The principal independent components of images [PDF]
, 2010 This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA).
Arlt, Björn, Brause, Rüdiger W.
core