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dichotomize    
v. 对分;二分;分叉

对分;二分;分叉

dichotomize
v 1: divide into two opposing groups or kinds [synonym:
{dichotomize}, {dichotomise}]


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  • Understanding the singular value decomposition (SVD)
    The Singular Value Decomposition (SVD) provides a way to factorize a matrix, into singular vectors and singular values Similar to the way that we factorize an integer into its prime factors to learn about the integer, we decompose any matrix into corresponding singular vectors and singular values to understand behaviour of that matrix
  • Why is the SVD named so? - Mathematics Stack Exchange
    The SVD stands for Singular Value Decomposition After decomposing a data matrix $\mathbf X$ using SVD, it results in three matrices, two matrices with the singular vectors $\mathbf U$ and $\mathbf V$, and one singular value matrix whose diagonal elements are the singular values But I want to know why those values are named as singular values
  • What is the intuitive relationship between SVD and PCA?
    $\begingroup$ Here is a link to a very similar thread on CrossValidated SE: Relationship between SVD and PCA How to use SVD to perform PCA? It covers similar grounds to J M 's answer (+1 by the way), but in somewhat more detail $\endgroup$ –
  • Understanding the proof of the Full SVD from the Economy SVD
    The proof below essentially derives the full scale SVD from the economy SVD $\textbf{Theorem (Full Scale
  • linear algebra - Full and reduced SVD of a 3x3 matrix. - Mathematics . . .
    I believe that this answers both b and c because this is the reduced SVD and it's regarding a square matrix, so it's already a full SVD? d and e First I calculate the matrices and then find the determinants of the upper left principals of the matrix, if they are all non-negative numbers, they will be positive semidefinite, if the
  • linear algebra - Singular Value Decomposition of Rank 1 matrix . . .
    I am trying to understand singular value decomposition I get the general definition and how to solve for the singular values of form the SVD of a given matrix however, I came across the following
  • Relation between SVD and EVD - Mathematics Stack Exchange
    Stack Exchange Network Stack Exchange network consists of 183 Q A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers
  • How does the SVD solve the least squares problem?
    Exploit SVD - resolve range and null space components A useful property of unitary transformations is that they are invariant under the $2-$ norm For example $$ \lVert \mathbf{V} x \rVert_{2} = \lVert x \rVert_{2} $$ This provides a freedom to transform problems into a form easier to manipulate
  • svd - What does singular value decomposition of covariance matrix . . .
    Keep in mind that because all covariance matrix are symmetric and positive semi-definite, their singular values are the same as their eigenvalues So you don't actually need to compute the SVD and can just directly compute the eigenvalues if you are interested in a rotation invariant measure of scale
  • To what extent is the Singular Value Decomposition unique?
    We know that the Polar Decomposition and the SVD are equivalent, but the polar decomposition is not unique unless the operator is invertible, therefore the SVD is not unique What is the difference between these uniquenesses?





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