LSA, VSM, & SVD






     

    Course Description

    This video series includes specific coverage of latent semantic analysis (LSA), vector space model (VSM), & singular value decomposition (SVD)

    The trade-offs of expanding the text analytics feature space with N-Grams.

    What You'll Learn

      >  How bag-of-words representations map to the vector space model.

      >  Usage of the dot product between document vectors as a proxy for correlation.

      >  LSA as a means to address the curse of dimensionality in text analytics.

      >  How LSA is implemented using SVD.

      >  Mapping new data into the lower dimensional SVD space.



     

    Text Analytics tutorial slides can be accessed here

    Download R here

    SMS Spam Collection Dataset used in this tutorial can be accessed here



     

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