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Both latent semantic analysis (LSA) and correspondence analysis (CA) aredimensionality reduction techniques that use singular value decomposition (SVD)for information retrieval. Theoretically, the results of LSA display both theassociation between documents and terms, and marginal effects; in comparison,CA only focuses on the associations between documents and terms. Marginaleffects are usually not relevant for information retrieval, and therefore, froma theoretical perspective CA is more suitable for information retrieval.  In this paper, we empirically compare LSA and CA. The elements of the rawdocument-term matrix are weighted, and the weighting exponent of singularvalues is adjusted to improve the performance of LSA. We explore whether thesetwo weightings also improve the performance of CA. In addition, we compare theoptimal singular value weighting exponents for LSA and CA to identify what theinitial dimensions in LSA correspond to.  The results for four empirical datasets show that CA always performs betterthan LSA. Weighting the elements of the raw data matrix can improve CA;however, it is data dependent and the improvement is small. Adjusting thesingular value weighting exponent usually improves the performance of CA;however, the extent of the improved performance depends on the dataset andnumber of dimensions. In general, CA needs a larger singular value weightingexponent than LSA to obtain the optimal performance. This indicates that CAemphasizes initial dimensions more than LSA, and thus, margins play animportant role in the initial dimensions in LSA.

Improving information retrieval through correspondence analysis instead of latent semantic analysis