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  2. Matrix difference equation - Wikipedia

    en.wikipedia.org/wiki/Matrix_difference_equation

    [1] [2] The order of the equation is the maximum time gap between any two indicated values of the variable vector. For example, = + is an example of a second-order matrix difference equation, in which x is an n × 1 vector of variables and A and B are n × n matrices. This equation is homogeneous because there is no vector constant term added ...

  3. Quotient space (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Quotient_space_(linear...

    The subspace, identified with R m, consists of all n-tuples such that the last n − m entries are zero: (x 1, ..., x m, 0, 0, ..., 0). Two vectors of R n are in the same equivalence class modulo the subspace if and only if they are identical in the last n − m coordinates. The quotient space R n /R m is isomorphic to R n−m in an obvious manner.

  4. Real coordinate space - Wikipedia

    en.wikipedia.org/wiki/Real_coordinate_space

    Cartesian coordinates identify points of the Euclidean plane with pairs of real numbers. In mathematics, the real coordinate space or real coordinate n-space, of dimension n, denoted R n or , is the set of all ordered n-tuples of real numbers, that is the set of all sequences of n real numbers, also known as coordinate vectors.

  5. Exterior algebra - Wikipedia

    en.wikipedia.org/wiki/Exterior_algebra

    A(rv, sw) = rsA(v, w) for any real numbers r and s, since rescaling either of the sides rescales the area by the same amount (and reversing the direction of one of the sides reverses the orientation of the parallelogram). A(v, v) = 0, since the area of the degenerate parallelogram determined by v (i.e., a line segment) is zero.

  6. Tensor product - Wikipedia

    en.wikipedia.org/wiki/Tensor_product

    The tensor product of two vector spaces is a vector space that is defined up to an isomorphism.There are several equivalent ways to define it. Most consist of defining explicitly a vector space that is called a tensor product, and, generally, the equivalence proof results almost immediately from the basic properties of the vector spaces that are so defined.

  7. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the ...

  8. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    As such, for two objects and having descriptors, the similarity is defined as: = = =, where the are non-negative weights and is the similarity between the two objects regarding their -th variable. In spectral clustering , a similarity, or affinity, measure is used to transform data to overcome difficulties related to lack of convexity in the ...

  9. Euclidean vector - Wikipedia

    en.wikipedia.org/wiki/Euclidean_vector

    The right-handedness constraint is necessary because there exist two unit vectors that are perpendicular to both a and b, namely, n and (−n). An illustration of the cross product The cross product a × b is defined so that a , b , and a × b also becomes a right-handed system (although a and b are not necessarily orthogonal ).