In brief

The lectures on Latent Semantic Analysis (LSA) are to be recorded through Wolfram University (Wolfram U) in December 2019 and January-February 2020.

The lectures

1. [X] Overview of LSA typical problems and basic workflows.
   Answering preliminary anticipated questions.

   • Here is the recording of the first session at Twitch .
   • What are the typical applications of LSA?
   • Why use LSA?
   • What it the fundamental philosophical or scientific assumption for LSA?
   • What is the most important and/or fundamental step of LSA?
   • What is the difference between LSA and Latent Semantic Indexing (LSI)?
   • What are the alternatives?
   • Using Neural Networks instead?
   • How is LSA used to derive similarities between two given texts?
   • How is LSA used to evaluate the proximity of phrases? (That have different words, but close semantic meaning.)
   • How the main dimension reduction methods compare?

2. [X] LSA for document collections.
   Here is the recording of the second session at Twitch: https://www.twitch.tv/videos/523306241 .

   • Motivational example -- full blown LSA workflow.

   • Fundamentals, text transformation (the hard way):

     • bag of words model,
     • stop words,
     • stemming.

   • The easy way with LSAMon.

   • "Eat your own dog food" example.

3. [X] Representation of the documents - the fundamental matrix object.
   Here is the recording of the third session at Twitch: https://www.twitch.tv/videos/533991174 .

   • Review: last session's example.

   • Review: the motivational example -- full blown LSA workflow.

   • Linear vector space representation:

     • LSA's most fundamental operation,
     • matrix with named rows and columns.

   • Pareto Principle adherence

     • for a document,
     • for a document collection, and
     • (in general.)

4. Representation of unseen documents.
   Here is the recording of the fourth session at Twitch.

   • Review: last session's matrix object.

     • Sparse matrix with named rows and columns.

   • Queries representation.

     • Representing rstudio-conf-2019 abstracts in the vector space of WTC-2019 abstracts.

   • Making a search engine for

     • [ ] [Raku's documentation](https://github.com/Raku/doc),
     • [X] WTC-2019 abstracts.

   • Dimension reduction over an image collection.

     • Topics over random mandalas.
     • Representation of unseen mandala images.

5. LSA for image de-noising and classification.
   Here is the recording of the fifth session at Twitch.

   • Review: last session's image collection topics extraction.
   • Let us try that two other datasets:
     • handwritten digits, and
     • Hentaigana (maybe).

   • Image denoising (maybe):

   • Using handwritten digits (again).

   • Image classification:

   • Handwritten digits.

6. [X] Further use cases. Here is the recording of the sixth session at Twitch.

   • Derive a custom taxonomy over a document collection.
     • Clustering with the reduced dimension.
   • Apply LSA to Great Conversation studies.
   • Use LSA for translation of natural languages.
   • Use LSA for making or improving search engines.

Attachments:

LSA-WolframU-liv...nb

LSA-WolframU-liv...nb

LSA-WolframU-liv...nb

LSA-WolframU-liv...nb

LSA-WolframU-liv...nb

LSA-WolframU-liv...nb