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Biplots provide a joint representation of observations and variables of a multidimensional matrix in the same reference system. In this subspace the relationships between them can be interpreted in terms of geometric elements. C<jats:sub>enet<\/jats:sub>Biplots projects a matrix onto a low-dimensional space generated simultaneously by sparse and orthogonal principal components. Sparsity is desired to select variables automatically, and orthogonality is necessary to keep the geometrical properties that ensure the biplots graphical interpretation. To this purpose, the present study focuses on two different objectives: 1) the extension of constrained singular value decomposition to incorporate an elastic net sparse constraint (C<jats:sub>enet<\/jats:sub>SVD), and 2) the implementation of C<jats:sub>enet<\/jats:sub>Biplots using C<jats:sub>enet<\/jats:sub>SVD. The usefulness of the proposed methodologies for analysing high-dimensional and low-dimensional matrices is shown. Our method is implemented in R software and available for download from<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/ananieto\/SparseCenetMA\">https:\/\/github.com\/ananieto\/SparseCenetMA<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s11634-021-00468-1","type":"journal-article","created":{"date-parts":[[2021,11,20]],"date-time":"2021-11-20T14:02:46Z","timestamp":1637416966000},"page":"5-19","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CenetBiplot: a new proposal of sparse and orthogonal biplots methods by means of elastic net CSVD"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2814-2807","authenticated-orcid":false,"given":"Nerea","family":"Gonz\u00e1lez-Garc\u00eda","sequence":"first","affiliation":[]},{"given":"Ana Bel\u00e9n","family":"Nieto-Librero","sequence":"additional","affiliation":[]},{"given":"Purificaci\u00f3n","family":"Galindo-Villard\u00f3n","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,20]]},"reference":[{"key":"468_CR1","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.compbiomed.2015.10.008","volume":"67","author":"ZY Algamal","year":"2015","unstructured":"Algamal ZY, Lee MH (2015) Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification. 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