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  2. Inner product space - Wikipedia

    en.wikipedia.org/wiki/Inner_product_space

    Inner product spaces generalize Euclidean vector spaces, in which the inner product is the dot product or scalar product of Cartesian coordinates. Inner product spaces of infinite dimension are widely used in functional analysis. Inner product spaces over the field of complex numbers are sometimes referred to as unitary spaces.

  3. Dot product - Wikipedia

    en.wikipedia.org/wiki/Dot_product

    In Euclidean geometry, the dot product of the Cartesian coordinates of two vectors is widely used. It is often called the inner product (or rarely the projection product) of Euclidean space, even though it is not the only inner product that can be defined on Euclidean space (see Inner product space for more).

  4. Euclidean distance - Wikipedia

    en.wikipedia.org/wiki/Euclidean_distance

    It can be extended to infinite-dimensional vector spaces as the L 2 norm or L 2 distance. [26] The Euclidean distance gives Euclidean space the structure of a topological space, the Euclidean topology, with the open balls (subsets of points at less than a given distance from a given point) as its neighborhoods. [27]

  5. Euclidean space - Wikipedia

    en.wikipedia.org/wiki/Euclidean_space

    The inner product of a Euclidean space is often called dot product and denoted x ⋅ y. This is specially the case when a Cartesian coordinate system has been chosen, as, in this case, the inner product of two vectors is the dot product of their coordinate vectors. For this reason, and for historical reasons, the dot notation is more commonly ...

  6. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    Then the Euclidean distance over the end-points of any two vectors is a proper metric which gives the same ordering as the cosine distance (a monotonic transformation of Euclidean distance; see below) for any comparison of vectors, and furthermore avoids the potentially expensive trigonometric operations required to yield a proper metric.

  7. Norm (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Norm_(mathematics)

    In particular, the Euclidean distance in a Euclidean space is defined by a norm on the associated Euclidean vector space, called the Euclidean norm, the 2-norm, or, sometimes, the magnitude or length of the vector. This norm can be defined as the square root of the inner product of a vector with itself.

  8. Riemannian manifold - Wikipedia

    en.wikipedia.org/wiki/Riemannian_manifold

    The dot product of two vectors tangent to the sphere sitting inside 3-dimensional Euclidean space contains information about the lengths and angle between the vectors. The dot products on every tangent plane, packaged together into one mathematical object, are a Riemannian metric.

  9. Hilbert space - Wikipedia

    en.wikipedia.org/wiki/Hilbert_space

    The real part of z, w is then the four-dimensional Euclidean dot product. This inner product is Hermitian symmetric, which means that the result of interchanging z and w is the complex conjugate: , = , ¯.

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