Talk:Latent semantic analysis

message: It is not possible that U and V are orthogonal matrices since they are not square matrices Nulli (talk) 14:53, 23 November 2017 (UTC)
thread_title:  Orthogonal matrices 

message: The section on the dimension reduction ("rank") seems to underemphasize the importance of this step. The dimension reduction isn't just some noise-reduction or cleanup step -- it is critical to the induction of meaning from the text. The full explanation is given in Landauer & Dumais (1997): A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge, published in Psychological Review. Any thoughts?
--Adono 02:54, 3 May 2006 (UTC)
thread_title: Rank

message: I added a section describing LSA using SVD. If LSA is viewed as something broader, and does not necessarily use SVD, I can move this elsewhere. -- Nils Grimsmo 18:16, 5 June 2006 (UTC)
thread_title:  Derivation 

message: Removed link to 'Google Uses LSA in Keyword Algorithms: Discussion' (which pointed to www.singularmarketing.com/latent_semantic_analysis ), as this seems to be an out of date link. —Preceding unsigned comment added by  88.108.193.212 (talk • contribs) 08:15, 4 September 2006
:I just removed two more obvious spam backlinks.  One unsigned link to a google book page, no IP, no nothing, just a mass of text and a link to a book (for sale) and the other was an 8 year old link to a dead and irrelevant blogger page.  I'm going to change this Section's Title to "Removing Spam Backlinks" as that seems to be something that people should regularly do here.Jonny Quick (talk) 04:23, 8 August 2015 (UTC)
thread_title: Removing Spam Backlinks

message: Many online and printed text resources describe the mechanics of the LSI via SVD, and the computational benefits of dimension reduction (reducing the rank of the transformed document-term space).
What these resources are not good at is talking about the intuitive interpretation of the transformed document-term space, and furthermore the dimension-reduced space.
It is not good enough to say that the procedure produces better results than pure term-matching information retrieval - it would be helpful to understand why.
perhaps a section called "intuitive interpretation". Bing Liu is the author who comes closest but doesn't succeed in my opinion .  —Preceding unsigned comment added by 82.68.244.150 (talk) 03:22, 4 October 2007 (UTC)
thread_title:  intuitive interpretation of transformed document-term space 

message: The current article states "After the construction of the occurrence matrix, LSA finds a low-rank approximation "
I'm not sure this is true. The LSA simply transforms the document-term matrix to a new set of basis axes. The SVD decomposition of the original matrix into 3 matrices is the LSA. The rank lowering is done afterwards by truncating the 3 SVD matrices. The LSA does not "find a low-rank approximation". The rank to which the matrices are lowered is set by the user, not by the LSA algorithm.  —Preceding unsigned comment added by 82.68.244.150 (talk) 03:54, 11 October 2007 (UTC)
thread_title:  "After the construction of the occurrence matrix, LSA finds a low-rank approximation " 

message: The "implementation" section currently says:
"A fast, incremental, low-memory, large-matrix SVD algorithm has recently been developed (Brand, 2006). Unlike Gorrell and Webb's (2005) stochastic approximation, Brand's (2006) algorithm provides an exact solution."
I don't think it's possible to have a polynomial-time exact solver for an SVD, because it reduces to root-finding; as with eigenvalue solvers.  So SVD solvers must be incremental until a tolerance is reached, in which case I'm not sure about this "exact solution" claim.  Glancing at the referred article, it looks like it's solving a much different problem than a general-purpose SVD algorithm.  Maybe I'm confused about the context of this "implementation" section.
As Latent Semantic Analysis is very nearly equivalent to Principal Components Analysis (PCA), and they both usually rely almost in whole on an SVD computation, maybe we could find some way to merge and/or link these articles together in some coherent fashion.  Any thoughts?  —Preceding unsigned comment added by Infzy (talk • contribs) 17:02, 17 August 2008 (UTC)
thread_title:  Exact solution? 

message: This page overlaps a lot with the Latent semantic indexing page - it even links there. 
Should they perhaps be merged? gromgull (talk) 17:44, 23 June 2009 (UTC)
replies: text: Yes, they should. They're actually near-synonyms; I believe the difference is that in LSI, one stores an LSA model in an index. Qwertyus (talk) 15:15, 13 July 2012 (UTC)

text: I agree. LSI was the term coined first as the SVD technique was used for document indexing and retrieval. LSA became used as the technique was applied to other textual analysis challenges such as recognizing synonyms. Dominic Widdows (talk) 16:43, 11 September 2012 (UTC)

text: It absolutely should. The Latent Semantic Indexing page explains critical methodology into the weighting scheme that is essential before computing SVD. LSA is incomplete without this invaluable information. Also, rank-reduction is well explained on the LSI page. Jobonki (talk) 19:38, 16 January 2014 (UTC)
thread_title:  Relation to Latent Semantic Indexing page? 

message: The original text is
:The vector \hat \textbf{t}_i then has k entries mapping it to a lower dimensional space dimensions. These new dimensions do not relate to any comprehensible concepts. They are a lower dimensional approximation of the higher dimensional space. Likewise, the vector \hat \textbf{d}_j is an approximation in this lower dimensional space.
It sounds like you're trying to say that \textbf{t}_i gets mapped to a lower dimensional space via the mapping \textbf{t}_i \rightarrow \hat\textbf{t}_i \in \mathbb{R}^k, right?
If so, then this paragraph should be cleaned up. What caught my attention at first was the redundant "dimensions" at the end of the first sentence. That should be fixed as well.
Justin Mauger (talk) 04:46, 21 October 2012 (UTC)
thread_title:  Derivation section: "mapping to lower dimensional space. 

message: The limitation "LSA cannot capture polysemy" contradicts "LSI overcomes two of the most problematic constraints of Boolean keyword queries: multiple words that have similar meanings (synonymy) and words that have more than one meaning (polysemy)." on the Latent_semantic_indexing page.
--Nicolamr (talk) 20:05, 31 July 2014 (UTC)
thread_title:  Polysemy? 

message: This paragraph is poorly written and confusing.
"In semantic hashing [14] documents are mapped to memory addresses by means of a neural network in such a way that semantically similar documents are located at nearby addresses."
Ok, makes sense.
"Deep neural network essentially builds a graphical model of the word-count vectors obtained from a large set of documents. "
What is a graphical model? What are word-count vectors? This terminology has not been used in the preceding part of the article. Are those just the counts of the words in those documents? Then in what sense are they obtained FROM a set of documents? And is it important that the set is large?
"Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document."
I get it, but the explanation is convoluted. It has to highlight that it's a faster/easier way to look for neighboring documents.
 "This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method."
This is a contradiction. How can semantic hashing be faster than locality sensitive hashing, if the latter is the fastest current method ?!
184.75.115.98 (talk) 17:00, 9 January 2017 (UTC)
thread_title: Semantic hashing

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thread_title:  External links modified 

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thread_title:  External links modified 

message: It's good and appropriate that the LSA and LSI pages have been merged, but the current result is quite confusing. Notably: - LSI is listed in the Alternative Methods section, even though it should now be framed as a synonym (as in the introduction) - after introducing LSI as an alternative method, the article proceeds to use LSI as the default term for its duration
As far as I can tell, the merge was basically a pure copy-paste job. This sort of thing predictably results in an article with confusing inconsistencies of terminology and redundancies of content. It badly needs a proper smoothing and revision pass by some generous soul with the requisite domain knowledge.  — Preceding unsigned comment added by Gfiddler (talk • contribs) 17:13, 17 October 2018 (UTC)
thread_title:  Needs cleanup after merge with LSI page