In PROC TEXTMINE, after SVD is applied using SVDINIT, what are the resulting SVD dimensions used for in subsequent analysis steps?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — a is correct because Singular Value Decomposition in PROC TEXTMINE reduces the high-dimensional sparse term-document matrix to a dense low-dimensional latent semantic space. These compact SVD dimensions capture underlying semantic patterns and are used as features for downstream tasks including clustering (e.g., PROC HPCLUS), classification, or as initialization for LDA topic modeling.
Full explanation below image
Full Explanation
A is correct because Singular Value Decomposition in PROC TEXTMINE reduces the high-dimensional sparse term-document matrix to a dense low-dimensional latent semantic space. These compact SVD dimensions capture underlying semantic patterns and are used as features for downstream tasks including clustering (e.g., PROC HPCLUS), classification, or as initialization for LDA topic modeling. B is wrong because term filtering happens in the PARSE step, not from SVD dimensions. C (readability score) is unrelated to SVD dimensions. D is wrong because SVD dimensions are semantic features, not random seeds.