7 A Review of Feature Reduction Methods …
137
63. Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Proceedings
of ICML workshop on unsupervised and transfer learning, Bellevue, 2 July 2012
64. Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. J Comput
Chem 38(16):1291–1307
65. Chandra B, Sharma RK (2015) Exploring autoencoders for unsupervised feature selection. In:
2015 international joint conference on neural networks (IJCNN), Killarney, 12–17 July 2015
66. Blaschke T, Olivecrona M, Engkvist O, Bajorath J, Chen H (2018) Application of generative
autoencoder in de novo molecular design. Mol Inform 37(1–2):1700123
67. Burgoon LD (2017) Autoencoder predicting estrogenic chemical substances (APECS): an
improved approach for screening potentially estrogenic chemicals using in vitro assays and
deep learning. Comput Toxicol 2:45–49
68. Ye J, Ji S (2009) Discriminant analysis for dimensionality reduction: an overview of recent
developments. In: Boulgouris NV, Plataniotis KN, Micheli-Tzanakou E (eds) Biometrics: theory, methods, and applications. IEEE Press, Piscataway, pp 1–20
69. Yan H, Dai Y (2011) The comparison of five discriminant methods. In: 2011 International
conference on management and service science, Wuhan, 12–14 August
70. Ren YY, Zhou LC, Yang L, Liu PY, Zhao BW, Liu HX (2016) Predicting the aquatic toxicity
mode of action using logistic regression and linear discriminant analysis. SAR QSAR Environ
Res 27(9):721–746
71. van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res
9:2579–2605
72. Borg I, Groenen PJF (2005) Modern Multidimensional Scaling, 2nd edn. Springer Science +
Business Media Inc, New York
73. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396
74. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear
dimensionality reduction. Science 290(5500):2319–2323
75. Izenman AJ (2012) Introduction to manifold learning. Wiley Interdiscip Rev Comput Stat
4(5):439–446
76. Kalousis A, Prados J, Hilario M (2007) Stability of feature selection algorithms: a study on
high-dimensional spaces. Knowl Inf Syst 12(1):95–116
77. Alelyani S, Liu H, Wang L (2011) The effect of the characteristics of the dataset on the selection
stability. In: 2011 IEEE 23rd international conference on tools with artificial intelligence, Boca
Raton, 7–9 November 2011
78. Yang P, Zhou BB, Yang JY-H, Zomaya AY (2013) Stability of feature selection algorithms
and ensemble feature selection methods in bioinformatics. In: Elloumi M, Zomaya AY (eds)
Biological knowledge discovery handbook: preprocessing, mining, and postprocessing of biological data. John Wiley & Sons Inc, Hoboken, pp 333–352
79. Yang P, Ho JW, Yang Y, Zhou BB (2011) Gene-gene interaction filtering with ensemble of
filters. BMC Bioinformatics 12:S10. https://doi.org/10.1186/1471-2105-12-S1-S10
80. Yang F, Mao KZ (2011) Robust feature selection for microarray data based on multicriterion
fusion. IEEE/ACM Trans Comput Biol Bioinforma 8(4):1080–1092
81. Abeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys Y (2010) Robust biomarker identification
for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26(3):392–398
82. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn.
Springer-Verlag, New York
83. Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray
gene-expression data. Proc Natl Acad Sci U S A 99(10):6562–6566
137
63. Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Proceedings
of ICML workshop on unsupervised and transfer learning, Bellevue, 2 July 2012
64. Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. J Comput
Chem 38(16):1291–1307
65. Chandra B, Sharma RK (2015) Exploring autoencoders for unsupervised feature selection. In:
2015 international joint conference on neural networks (IJCNN), Killarney, 12–17 July 2015
66. Blaschke T, Olivecrona M, Engkvist O, Bajorath J, Chen H (2018) Application of generative
autoencoder in de novo molecular design. Mol Inform 37(1–2):1700123
67. Burgoon LD (2017) Autoencoder predicting estrogenic chemical substances (APECS): an
improved approach for screening potentially estrogenic chemicals using in vitro assays and
deep learning. Comput Toxicol 2:45–49
68. Ye J, Ji S (2009) Discriminant analysis for dimensionality reduction: an overview of recent
developments. In: Boulgouris NV, Plataniotis KN, Micheli-Tzanakou E (eds) Biometrics: theory, methods, and applications. IEEE Press, Piscataway, pp 1–20
69. Yan H, Dai Y (2011) The comparison of five discriminant methods. In: 2011 International
conference on management and service science, Wuhan, 12–14 August
70. Ren YY, Zhou LC, Yang L, Liu PY, Zhao BW, Liu HX (2016) Predicting the aquatic toxicity
mode of action using logistic regression and linear discriminant analysis. SAR QSAR Environ
Res 27(9):721–746
71. van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res
9:2579–2605
72. Borg I, Groenen PJF (2005) Modern Multidimensional Scaling, 2nd edn. Springer Science +
Business Media Inc, New York
73. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396
74. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear
dimensionality reduction. Science 290(5500):2319–2323
75. Izenman AJ (2012) Introduction to manifold learning. Wiley Interdiscip Rev Comput Stat
4(5):439–446
76. Kalousis A, Prados J, Hilario M (2007) Stability of feature selection algorithms: a study on
high-dimensional spaces. Knowl Inf Syst 12(1):95–116
77. Alelyani S, Liu H, Wang L (2011) The effect of the characteristics of the dataset on the selection
stability. In: 2011 IEEE 23rd international conference on tools with artificial intelligence, Boca
Raton, 7–9 November 2011
78. Yang P, Zhou BB, Yang JY-H, Zomaya AY (2013) Stability of feature selection algorithms
and ensemble feature selection methods in bioinformatics. In: Elloumi M, Zomaya AY (eds)
Biological knowledge discovery handbook: preprocessing, mining, and postprocessing of biological data. John Wiley & Sons Inc, Hoboken, pp 333–352
79. Yang P, Ho JW, Yang Y, Zhou BB (2011) Gene-gene interaction filtering with ensemble of
filters. BMC Bioinformatics 12:S10. https://doi.org/10.1186/1471-2105-12-S1-S10
80. Yang F, Mao KZ (2011) Robust feature selection for microarray data based on multicriterion
fusion. IEEE/ACM Trans Comput Biol Bioinforma 8(4):1080–1092
81. Abeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys Y (2010) Robust biomarker identification
for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26(3):392–398
82. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn.
Springer-Verlag, New York
83. Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray
gene-expression data. Proc Natl Acad Sci U S A 99(10):6562–6566