Fisher Linear Discriminant Analysis

By Cheng Li. 2014

195 citations recorded by the source corpus.

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Citations in the corpus

Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “MDA is the extended version of the Linear Discriminant Analysis (LDA) which utilizes the Fisher criterion [31] as the optimal criterion of the learnt subspace.”
Measurements of the properties of the Higgs-like boson in the four lepton decay channel with the ATLAS detector using 25 fb$^{-1}$ of proton-proton collision data (2013, unknown): “[38] and Fisher Linear Discriminant [55].”
Linear discriminant analysis: A detailed tutorial (2017, unknown): “The LDA technique is developed to transform the features into a lower dimensional space, which maximizes the ratio of the between-class variance to the within-class variance, thereby guaranteeing maximum class separability [43,76].”
How Generative Adversarial Networks and Their Variants Work (2017, background): “In addition to standard IPM in Equation 9, Fisher GAN [84] incorporates a data-dependent constraint by the following equations: dF (pdata, pg) = sup f∈F Ex∼pdata f(x)− Ex∼pg f(x)√ 1 2 (Ex∼pdata f2(x) + Ex∼pg f2(x)) (23) = sup f∈F, 12 [Ex∼pdata f2(x)+Ex∼pg f2(x)]=1 Ex∼pdata f(x)− Ex∼pg f(x) (24) Equa”
How Generative Adversarial Networks and Its Variants Work : An Overview of GAN (2017, background): “Instead of using standard IPM in Equation 11, Fisher GAN [79] emphasizes a standardized mean discrepancy which naturally induces a data-dependent constraint by the following equations: dF (pdata, pθ) = sup f∈F Ex∼pdata f(x)− Ex∼pθ f(x)√ 1 2 (Ex∼pdata f2(x) + Ex∼pθ f2(x)) (26) = sup f∈F, 12 Ex∼pdata ”