The weighted inner product is a generalization of the standard inner product, where each dimension of the vectors involved is scaled by a corresponding weight. This allows for more flexible similarity measures in applications such as machine learning, where different features may have varying levels of importance.

Weighted Inner Product

weighted inner product

generalization of the standard inner product

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