# GHA update rule

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The Generalized Hebbian Algorithm (GHA) update rule (also known as Sanger's rule in the literature) is a Hebbian type update rule that performs PCA. It is similar to Oja's rule, but converges to ordered principal components.

 GHA Family Hebbian Direction Forward Deployable Yes Supervised No

## Usage

The Generalized Hebbian Algorithm tunes a Hebbian layer so that its weights form ordered principal components. The principal components are basis vectors that are aligned so that the greatest variance by any projection of the data comes to lie on the first component, the second greatest variance on the second component and so on.

## Algorithm

where is the learning rate.

## Settings

The settings can be modified using the settings browser.

GHA update rule settings

• (Learning Forward)
• Step: The size of the learning rate.