نتایج جستجو برای: PCa

تعداد نتایج: 23925  

Journal: :Artificial Intelligence Review 2022

Abstract Principal Component Analysis (PCA) is one of the most widely used data analysis methods in machine learning and AI. This manuscript focuses on mathematical foundation classical PCA its application to a small-sample-size scenario large dataset high-dimensional space scenario. In particular, we discuss simple method that can be approximate latter case. also help kernel or (KPCA) for larg...

Journal: :The Annals of Applied Statistics 2018

Journal: :Computer Science & Engineering: An International Journal 2012

Journal: :CoRR 2016
Francisco J. Soulignac Pablo Terlisky

A proper circular-arc (PCA) model is a pairM = (C,A) where C is a circle and A is a family of inclusion-free arcs on C in which no two arcs of A cover C. A PCA model U is a (c, `, d, ds)-CA model when C has circumference c, all the arcs in A have length `, all the extremes of the arcs in A are at a distance at least d, and all the beginning points of the arcs in A are at a distance at least d+ ...

Journal: :THE JOURNAL OF JAPAN SOCIETY FOR CLINICAL ANESTHESIA 2011

Journal: :JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 1993

Journal: :Comput. Graph. Forum 2016
Naoki Kita Kazunori Miyata

Algorithm 1 Suggesting Compatible Colors 1: procedure COMPATIBLECOLORS(palette t, index k, #cands Ncand, #samples Nsample, threshold (τ, κ)) 2: . Sampling candidate’s HSVs 3: f ← COMPUTEHUEPROBABILITY(t, k) . Eq. 3 or Eq. 4 4: hi← SAMPLINGFROMHUEPROB( f , Nsample) 5: si ∼N (μs,σs) . §4.2 6: vi ∼N (μv,σv) . §4.2 7: . Compute rating 8: for i = 1→ m do 9: ci← (hi, si,vi) 10: Ccand i ← COMPATIBLECA...

Journal: :Canadian journal of statistics 2023

Mahalanobis distance of covariate means between treatment and control groups is often adopted as a balance criterion when implementing rerandomization strategy. However, this may not work well for high-dimensional cases because it balances all orthogonalized covariates equally. We propose using principal component analysis (PCA) to identify proper subspaces in which should be calculated. Not on...

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