Abstract
In multiple correspondence analysis (MCA), an estimated solution can be transformed into a simple structure in order to simplify the interpretation. The rotation technique is widely used for this purpose. However, an alternative approach, called sparse MCA, has also been proposed. One of the advantages of sparse MCA is that, in contrast to unrotated or rotated ordinary MCA loadings, some loadings in sparse MCA can be exactly zero. A real data example demonstrates that sparse MCA can provide simple solutions.
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Mori, Y., Kuroda, M., Makino, N. (2016). Sparse Multiple Correspondence Analysis. In: Nonlinear Principal Component Analysis and Its Applications. SpringerBriefs in Statistics(). Springer, Singapore. https://doi.org/10.1007/978-981-10-0159-8_5
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DOI: https://doi.org/10.1007/978-981-10-0159-8_5
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