THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTS

By R. Fisher. 1938

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Citations in the corpus

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, methodology): “New features can be extracted by ﬁnding linear combinations of the original ones, as in PCA ( Principal Component Analysis ) [29, 30] or LDA ( Linear Discriminant Analysis ) [31], as well as nonlinear combinations, like Kernel PCA [32] or Isomap [33].”
Data analysis for electronic nose systems (2006, methodology): “Discriminant function analysis (DFA) techniques are supervised and multivariate, they were first reported by Fisher [72, 73 ] on the well known iris data of Anderson [74].”
Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis (2009, methodology): “The authors report that PCA outperformed LDA for the intent recognition between standing and walking classes for a 3-”
The performance of insolvency prediction and credit risk models in the UK : a comparative study, development and wider application (2012, unknown): “155 A more direct approach to this special case is provided by Fisher (1938) in which the resulting equations”
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria (2001, methodology): “The most well-known technique for linear dimension reduction (LDR) in the K-class problem is linear discriminant analysis (LDA) (Fisher [ 5 ] introduced two-class LDA, while Rao [13] generalized LDA to multiple classes): A transformation matrix from an n-dimensional feature space to a d-dimensional ”
Face recognition: A literature survey (2003, methodology): “…[Belhumeur et al. 1997; Liu and Wechsler 2001; Swets and Weng 1996b; Zhao et al. 1998] which use linear/Fisher discriminant analysis (FLD/LDA) [Fisher 1938]; Bayesian methods, which use a probabilistic distance metric [Moghaddam and Pentland 1997]; and SVM methods, which use a…”
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition (2002, methodology): “On the other hand, the discriminant analysis based on the Fisher’s linear discriminant function [8] was widely employed in face recognition.”
Face Recognition (2018, unknown): “The speedup due to the hierarchical space (represented by a tree) is O(n=log(n)).”
Compound facial expressions of emotion (2014, background): “Du et al. PNAS Early Edition | 5 of 9 CO M PU TE R SC IE N CE S PH YS IO LO G Y PN A S PL U S features given by the eigenvector v1 of the LDA equation S−1W SBV=VΛ, where SW = Pc i=1 Pmi k=1ðxik − μiÞðxik − μiÞT is the within-class scatter matrix, SB =m−1 Pc i=1miðμi − μÞðμi − μÞT is the between-clas”
PCA versus LDA (2001, unknown): “Linear Discriminant Analysis (LDA) [3], [4] searches for those vectors in the underlying space that best discriminate among classes (rather than those that best describe the data).”
An introduction to domain adaptation and transfer learning (2018, unknown): “The Fisher criterion consists of the ratio of between-class scatter, SB = ∑K k πk(μk − μ̄)(μk − μ̄) ⊤ with μk the mean of the k-th class, μ̄ the overall mean and πk the class-prior, and average within-class scatter, SW = ∑K k πkΣk with ΣK as the covariance matrix of the k-th class [67,148].”
Subclass discriminant analysis (2006, methodology): “FEATURE extraction via Discriminant Analysis (DA) is oneof the most sought after approaches in applications in computer vision [9], [24], [7], [3], [19].”