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Angle Principal Component Analysis [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Recently, many ℓ1-norm based PCA methods have been developed for dimensionality reduction, but they do not explicitly consider the reconstruction error. Moreover, they do not take into account the relationship between reconstruction error and variance of projected data. This reduces the robustness of algorithms.
Qianqian Wang 0001 +3 more
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FEBS Letters, EarlyView.The Ile181Asn variant of human UDP‐xylose synthase (hUXS1), associated with a short‐stature genetic syndrome, has previously been reported as inactive. Our findings demonstrate that Ile181Asn‐hUXS1 retains catalytic activity similar to the wild‐type but exhibits reduced stability, a looser oligomeric state, and an increased tendency to precipitate ...
Tuo Li +2 more
wiley +1 more source
Demonstrating the Mechanics of Principal Component Analysis via Spreadsheets [PDF]
, 2019 Principal component analysis (PCA) is a popular multivariate statistical method that is used for dimensionality reduction. When teaching PCA in a marketing research or business analytics course, the mechanics of the analysis are often not communicated to
Michael Brusco
core
A Tutorial on Principal Component Analysis
CoRR, 2014 Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood. The goal of this paper is to dispel the magic behind this black box. This manuscript focuses on building a solid intuition for how and why principal component analysis works.
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Binary Principal Component Analysis [PDF]
Procedings of the British Machine Vision Conference 2006, 2006 Efficient and compact representation of images is a fundamental problem in computer vision. Principal Component Analysis (PCA) has been widely used for image representation and has been successfully applied to many computer vision algorithms. In this paper, we propose a method that uses Haar-like binary box functions to span a subspace which ...
Feng Tang, Hai Tao
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FEBS Letters, EarlyView.We reconstituted Synechocystis glycogen synthesis in vitro from purified enzymes and showed that two GlgA isoenzymes produce glycogen with different architectures: GlgA1 yields denser, highly branched glycogen, whereas GlgA2 synthesizes longer, less‐branched chains.
Kenric Lee +3 more
wiley +1 more source
Wind forecasting using Principal Component Analysis
, 2014 We present a new statistical wind forecasting tool based on Principal Component Analysis (PCA), which is trained on past data to predict the wind speed using an ensemble of dynamically similar past events.
Fruh, Wolf-Gerrit; id_orcid +3 more
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FEBS Letters, EarlyView.We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley +1 more source
State-space independent component analysis for nonlinear dynamic process monitoring [PDF]
, 2010 The cost effective benefits of process monitoring will never be over emphasised. Amongst monitoring techniques, the Independent Component Analysis (ICA) is an efficient tool to reveal hidden factors from process measurements, which follow non-Gaussian
Odiowei, P. P., Cao, Yi
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Bandit Principal Component Analysis
CoRR, 2019 We consider a partial-feedback variant of the well-studied online PCA problem where a learner attempts to predict a sequence of $d$-dimensional vectors in terms of a quadratic loss, while only having limited feedback about the environment's choices. We focus on a natural notion of bandit feedback where the learner only observes the loss associated with
Wojciech Kotlowski, Gergely Neu
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