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  2. Normalized frequency (signal processing) - Wikipedia

    en.wikipedia.org/wiki/Normalized_frequency...

    Therefore, the normalized frequency unit is important when converting normalized results into physical units. Example of plotting samples of a frequency distribution in the unit "bins", which are integer values. A scale factor of 0.7812 converts a bin number into the corresponding physical unit (hertz).

  3. Unit vector - Wikipedia

    en.wikipedia.org/wiki/Unit_vector

    In mathematics, a unit vector in a normed vector space is a vector (often a spatial vector) of length 1. A unit vector is often denoted by a lowercase letter with a circumflex, or "hat", as in ^ (pronounced "v-hat"). The term normalized vector is sometimes used as a synonym for unit vector.

  4. Orthonormality - Wikipedia

    en.wikipedia.org/wiki/Orthonormality

    A unit vector means that the vector has a length of 1, which is also known as normalized. Orthogonal means that the vectors are all perpendicular to each other. A set of vectors form an orthonormal set if all vectors in the set are mutually orthogonal and all of unit length.

  5. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    Cosine similarity can be seen as a method of normalizing document length during comparison. In the case of information retrieval , the cosine similarity of two documents will range from 0 → 1 {\displaystyle 0\to 1} , since the term frequencies cannot be negative.

  6. Normalization (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(machine...

    Query-Key normalization (QKNorm) [32] normalizes query and key vectors to have unit L2 norm. In nGPT , many vectors are normalized to have unit L2 norm: [ 33 ] hidden state vectors, input and output embedding vectors, weight matrix columns, and query and key vectors.

  7. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines , logistic regression , and artificial neural networks ).

  8. Sinc function - Wikipedia

    en.wikipedia.org/wiki/Sinc_function

    In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by ⁡ = ⁡ (). In either case, the value at x = 0 is defined to be the limiting value sinc ⁡ 0 := lim x → 0 sin ⁡ ( a x ) a x = 1 {\displaystyle \operatorname {sinc} 0:=\lim _{x\to 0}{\frac {\sin(ax)}{ax}}=1} for all real a ...

  9. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    Top: The action of M, indicated by its effect on the unit disc D and the two canonical unit vectors e 1 and e 2. Left: The action of V ⁎, a rotation, on D, e 1, and e 2. Bottom: The action of Σ, a scaling by the singular values σ 1 horizontally and σ 2 vertically. Right: The action of U, another rotation.