Leituras recomendadas

As leituras abaixo não são pré-requisitos para acompanhar o livro. Elas são boas portas de entrada quando você quiser ver uma apresentação mais completa, outra notação ou uma discussão mais longa de algum método.

Candès, Emmanuel J., Xiaodong Li, Yi Ma, e John Wright. 2011. “Robust Principal Component Analysis?” Journal of the ACM 58 (3): 1–37. https://doi.org/10.1145/1970392.1970395.

Greenacre, Michael. 2017. Correspondence Analysis in Practice. 3º ed. CRC Press.

Hair, Joseph F., William C. Black, Barry J. Babin, e Rolph E. Anderson. 2019. Multivariate Data Analysis. 8º ed. Cengage.

Harman, Harry H. 1976. Modern Factor Analysis. 3º ed. University of Chicago Press.

Hastie, Trevor, Robert Tibshirani, e Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2º ed. Springer.

Horn, John L. 1965. “A Rationale and Test for the Number of Factors in Factor Analysis”. Psychometrika 30 (2): 179–85. https://doi.org/10.1007/BF02289447.

Hubert, Mia, Peter J. Rousseeuw, e Karlien Vanden Branden. 2005. “ROBPCA: A New Approach to Robust Principal Component Analysis”. Technometrics 47 (1): 64–79. https://doi.org/10.1198/004017004000000563.

Johnson, Richard A., e Dean W. Wichern. 2007. Applied Multivariate Statistical Analysis. 6º ed. Pearson Prentice Hall.

Jolliffe, Ian T. 2002. Principal Component Analysis. 2º ed. Springer.

Maaten, Laurens van der, e Geoffrey Hinton. 2008. “Visualizing Data Using t-SNE”. Journal of Machine Learning Research 9: 2579–605.

Mardia, Kanti V., John T. Kent, e John M. Bibby. 1979. Multivariate Analysis. Academic Press.

McInnes, Leland, John Healy, e James Melville. 2018. “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction”. arXiv preprint arXiv:1802.03426, publicação prévia em linha. https://doi.org/10.48550/arXiv.1802.03426.

McLachlan, Geoffrey J., e David Peel. 2000. Finite Mixture Models. Wiley.

Rencher, Alvin C. 2002. Methods of Multivariate Analysis. 2º ed. Wiley.

Schölkopf, Bernhard, Alexander Smola, e Klaus-Robert Müller. 1998. “Nonlinear Component Analysis as a Kernel Eigenvalue Problem”. Neural Computation 10 (5): 1299–319. https://doi.org/10.1162/089976698300017467.

Tipping, Michael E., e Christopher M. Bishop. 1999. “Probabilistic Principal Component Analysis”. Journal of the Royal Statistical Society: Series B 61 (3): 611–22. https://doi.org/10.1111/1467-9868.00196.

Zou, Hui, Trevor Hastie, e Robert Tibshirani. 2006. “Sparse Principal Component Analysis”. Journal of Computational and Graphical Statistics 15 (2): 265–86. https://doi.org/10.1198/106186006X113430.