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fitlsa

Fit LSA model

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Syntax

mdl = fitlsa(bag,numComponents)
mdl = fitlsa(counts,numComponents)
mdl = fitlsa(___,Name,Value)

Description

A latent semantic analysis (LSA) model discovers relationships between documents and the words that they contain. An LSA model is a dimensionality reduction tool useful for running low-dimensional statistical models on high-dimensional word counts. If the model was fit using a bag-of-n-grams model, then the software treats the n-grams as individual words.

example

mdl = fitlsa(bag,numComponents) fits an LSA model with numComponents components to the bag-of-words or bag-of-n-grams model bag.

example

mdl = fitlsa(counts,numComponents) fits an LSA model to the documents represented by the matrix of word counts counts.

example

mdl = fitlsa(___,Name,Value) specifies additional options using one or more name-value pair arguments.

Examples

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Open Live Script

Fit a Latent Semantic Analysis model to a collection of documents.

Load the example data. The file sonnetsPreprocessed.txt contains preprocessed versions of Shakespeare's sonnets. The file contains one sonnet per line, with words separated by a space. Extract the text from sonnetsPreprocessed.txt, split the text into documents at newline characters, and then tokenize the documents.

filename = "sonnetsPreprocessed.txt";
str = extractFileText(filename);
textData = split(str,newline);
documents = tokenizedDocument(textData);

Create a bag-of-words model using bagOfWords.

bag = bagOfWords(documents) 
bag = 
  bagOfWords with properties:

          Counts: [154x3092 double]
      Vocabulary: ["fairest"    "creatures"    "desire"    "increase"    "thereby"    "beautys"    "rose"    "might"    "never"    "die"    "riper"    "time"    "decease"    "tender"    "heir"    "bear"    "memory"    "thou"    ...    ] (1x3092 string)
        NumWords: 3092
    NumDocuments: 154

Fit an LSA model with 20 components.

numComponents = 20;
mdl = fitlsa(bag,numComponents)
mdl = 
  lsaModel with properties:

              NumComponents: 20
           ComponentWeights: [2.7866e+03 515.5889 443.6428 316.4191 295.4065 261.8927 226.1649 186.2160 170.6413 156.6033 151.5275 146.2553 141.6741 135.5318 134.1694 128.9931 124.2382 122.2931 116.5035 116.2590]
             DocumentScores: [154x20 double]
                 WordScores: [3092x20 double]
                 Vocabulary: ["fairest"    "creatures"    "desire"    "increase"    "thereby"    "beautys"    "rose"    "might"    "never"    "die"    "riper"    "time"    "decease"    "tender"    "heir"    "bear"    "memory"    ...    ] (1x3092 string)
    FeatureStrengthExponent: 2

Transform new documents into lower dimensional space using the LSA model.

newDocuments = tokenizedDocument([
    "what's in a name? a rose by any other name would smell as sweet."
    "if music be the food of love, play on."]);
dscores = transform(mdl,newDocuments)
dscores = 2×20

    0.1338    0.1623    0.1680   -0.0541   -0.2464    0.0134    0.2604    0.0205   -0.1127    0.0627    0.3311   -0.2327    0.1689   -0.2695    0.0228    0.1241    0.1198    0.2535   -0.0607    0.0305
    0.2547    0.5576   -0.0095    0.5660   -0.0643    0.1236   -0.0082   -0.0522    0.0690   -0.0330    0.0385    0.0803   -0.0373    0.0384   -0.0005    0.1943    0.0207    0.0278    0.0001   -0.0469
Open Live Script

Load the example data. sonnetsCounts.mat contains a matrix of word counts corresponding to preprocessed versions of Shakespeare's sonnets.

load sonnetsCounts.mat
size(counts)
ans = 1×2

         154        3092

Fit LSA model with 20 components. Set the feature strength exponent to 4.

numComponents = 20;
exponent = 4;
mdl = fitlsa(counts,numComponents, ...
    'FeatureStrengthExponent',exponent)
mdl = 
  lsaModel with properties:

              NumComponents: 20
           ComponentWeights: [2.7866e+03 515.5889 443.6428 316.4191 295.4065 261.8927 226.1649 186.2160 170.6413 156.6033 151.5275 146.2553 141.6741 135.5318 134.1694 128.9931 124.2382 122.2931 116.5035 116.2590]
             DocumentScores: [154x20 double]
                 WordScores: [3092x20 double]
                 Vocabulary: ["1"    "2"    "3"    "4"    "5"    "6"    "7"    "8"    "9"    "10"    "11"    "12"    "13"    "14"    "15"    "16"    "17"    "18"    "19"    "20"    "21"    "22"    "23"    "24"    "25"    "26"    ...    ] (1x3092 string)
    FeatureStrengthExponent: 4

Input Arguments

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Input bag-of-words or bag-of-n-grams model, specified as a bagOfWords object or a bagOfNgrams object. If bag is a bagOfNgrams object, then the function treats each n-gram as a single word.

Number of components, specified as a positive integer. This value must be less than the number of the input documents, and the vocabulary size of the input documents.

Example: 200

Frequency counts of words, specified as a matrix of nonnegative integers. If you specify 'DocumentsIn' to be 'rows', then the value counts(i,j) corresponds to the number of times the jth word of the vocabulary appears in the ith document. Otherwise, the value counts(i,j) corresponds to the number of times the ith word of the vocabulary appears in the jth document.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: 'FeatureStrengthExponent',4 sets the feature strength exponent to 4.

Orientation of documents in the word count matrix, specified as the comma-separated pair consisting of 'DocumentsIn' and one of the following:

  • 'rows' – Input is a matrix of word counts with rows corresponding to documents.

  • 'columns' – Input is a transposed matrix of word counts with columns corresponding to documents.

This option only applies if you specify the input documents as a matrix of word counts.

Note

If you orient your word count matrix so that documents correspond to columns and specify 'DocumentsIn','columns', then you might experience a significant reduction in optimization-execution time.

Initial feature strength exponent, specified as a nonnegative scalar. This value scales the feature component strengths for the documentScores, wordScores, and transform functions.

Example: 'FeatureStrengthExponent',4

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Output Arguments

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Output LSA model, returned as an lsaModel object.

Version History

Introduced in R2017b

See Also

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