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Graph Mining Notes.pdf

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🔗 Source: math.cmu.edu
📊 Size: 1.05 MB
📄 Pages: 12 pages
⬇️ Downloads: 136

Summary

Matrix vector multiplication can be viewed as an operation that returns a vector whose coordinates are given by the inner products of the rows of the matrix with the vector, or as a linear combination of the columns of the matrix. The inverse of a square matrix can be applied to a vector to obtain the coordinates of the vector in a different basis. Orthogonality implies independence, and is a fundamental concept in linear algebra. Vector norms, such as the 2-norm, and matrix norms, such as the induced 2-norm and the Frobenius norm, are used to measure the size of vectors and matrices. The Singular Value Decomposition (SVD) of a matrix can be used for dimensionality reduction, and is optimal with respect to the L2 and Frobenius norm. The SVD can also be used for image compression, and randomized algorithms can be used to speed up the computation. The HITS algorithm uses the SVD to rank web pages given a query, by computing the hubs and authorities in the graph.

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Description

Matrix vector multiplication can be viewed as an operation that returns a vector whose coordinates are given by the inner products of the rows of the matrix...

Technical Information

  • File Format: PDF
  • File Size: 1.05 MB
  • Pages: 12
  • Language: EN
  • Total Downloads: 136
  • Last Updated: 3 weeks ago

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This PDF document about Graph Mining Notes provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Graph Mining Notes.

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