Abstract
Feature selection is the process of reducing the number of collected features to a relevant subset of features and is often used to combat the curse of dimensionality. This paper provides a review of the literature on feature selection techniques specifically designed for Gaussian mixture models (GMMs) and hidden Markov models (HMMs), two common parametric latent variable models. The primary contribution of this work is the collection and grouping of feature selection methods specifically designed for GMMs and for HMMs. An additional contribution lies in outlining the connections between these two groups of feature selection methods. Often, feature selection methods for GMMs and HMMs are treated as separate topics. In this survey, we propose that methods developed for one model can be adapted to the other model. Further, we find that the number of feature selection methods for GMMs outweighs the number of methods for HMMs and that the proportion of methods for HMMs that require supervised data is larger than the proportion of GMM methods that require supervised data. We conclude that further research into unsupervised feature selection methods for HMMs is required and that established methods for GMMs could be adapted to HMMs. It should be noted that feature selection can also be referred to as dimensionality reduction, variable selection, attribute selection, and variable subset reduction. In this paper, we make a distinction between dimensionality reduction and feature selection. Dimensionality reduction, which we do not consider, is any process that reduces the number of features used in a model and can include methods that transform features in order to reduce the dimensionality. Feature selection, by contrast, is a specific form of dimensionality reduction that eliminates feature as inputs into the model. The primary difference is that dimensionality reduction can still require the collection of all the data sources in order to transform and reduce the feature set, while feature selection eliminates the need to collect the irrelevant data sources.
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References
Adams S, Beling PA, Cogill R (2016) Feature selection for hidden Markov models and hidden semi-Markov models. IEEE Access 4:1642–1657
Aha DW, Bankert RL (1995) A comparative evaluation of sequential feature selection algorithms. In: Proceedings of the fifth international workshop on artificial intelligence and statistics
Allili MS, Bouguila N, Ziou D (2008) Finite general Gaussian mixture modeling and application to image and video foreground segmentation. J Electron Imaging 17(1):013,005–013,005
Allili MS, Ziou D, Bouguila N, Boutemedjet S (2010) Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection. IEEE Trans Circuits Syst Video Technol 20(10):1373–1377
Almuallim H, Dietterich TG (1991) Learning with many irrelevant features. In: AAAI, vol 91. Citeseer, pp 547–552
Bagos PG, Liakopoulos TD, Hamodrakas SJ (2004) Faster gradient descent training of hidden Markov models, using individual learning rate adaptation. In: International colloquium on grammatical inference. Springer, pp 40–52
Bahl L, Brown PF, De Souza PV, Mercer RL (1986) Maximum mutual information estimation of hidden Markov model parameters for speech recognition. In: Proceedings of ICASSP, vol 86, pp 49–52
Bashir FI, Khokhar AA, Schonfeld D (2007) Object trajectory-based activity classification and recognition using hidden Markov models. IEEE Trans Image Process 16(7):1912–1919
Bhattacharya S, McNicholas PD (2014) A LASSO-penalized BIC for mixture model selection. Adv Data Anal Classif 8(1):45–61
Bilmes J (1998) A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Int Comput Sci Inst 4(510):126
Bins J, Draper BA (2001) Feature selection from huge feature sets. In: Eighth IEEE international conference on computer vision, 2001. ICCV 2001. Proceedings, vol 2. IEEE, pp 159–165
Bishop CM, Hinton GE, Strachant IG (1997) GTM through time. In: Proceedings of the IEEE fifth international conference on artificial neural networks. Citeseer
Bishop CM, Svensén M, Williams CK (1998) GTM: the generative topographic mapping. Neural Comput 10(1):215–234
Blum AL, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97(1):245–271
Bocchieri E (1993) Vector quantization for the efficient computation of continuous density likelihoods. In: 1993 IEEE international conference on acoustics, speech, and signal processing, 1993. ICASSP-93, vol 2. IEEE, pp 692–695
Boutemedjet S, Bouguila N, Ziou D (2007) Feature selection for non Gaussian mixture models. In: 2007 IEEE workshop on machine learning for signal processing. IEEE, pp 69–74
Bouveyron C, Brunet C (2012) Simultaneous model-based clustering and visualization in the fisher discriminative subspace. Stat Comput 22(1):301–324
Bouveyron C, Brunet-Saumard C (2014) Discriminative variable selection for clustering with the sparse Fisher-EM algorithm. Comput Stat 29(3–4):489–513
Boys RJ, Henderson DA (2001) A comparison of reversible jump MCMC algorithms for DNA sequence segmentation using hidden Markov models. Comput Sci Stat 33:35–49
Cappé O, Buchoux V, Moulines E (1998) Quasi-Newton method for maximum likelihood estimation of hidden Markov models. In: Proceedings of the 1998 IEEE international conference on acoustics, speech and signal processing, 1998, vol 4. IEEE, pp 2265–2268
Carbonetto P, De Freitas N, Gustafson P, Thompson N (2003) Bayesian feature weighting for unsupervised learning, with application to object recognition. In: Artificial intelligence and statistics (AI & Statistics’ 03). Society for Artificial Intelligence and Statistics
Caruana R, Freitag D (1994) Greedy attribute selection. In: ICML. Citeseer, pp 28–36
Caruana R, Freitag D (1994) How useful is relevance? FOCUS 14(8):2
Celeux G, Martin-Magniette ML, Maugis-Rabusseau C, Raftery AE (2014) Comparing model selection and regularization approaches to variable selection in model-based clustering. Journal de la Societe francaise de statistique (2009) 155(2):57
Chang S, Dasgupta N, Carin L (2005) A Bayesian approach to unsupervised feature selection and density estimation using expectation propagation. In: IEEE Computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 2. IEEE, pp 1043–1050
Charlet D, Jouvet D (1997) Optimizing feature set for speaker verification. In: International conference on audio-and video-based biometric person authentication. Springer, pp 203–210
Chatzis SP, Kosmopoulos DI (2011) A variational Bayesian methodology for hidden Markov models utilizing Student’s-t mixtures. Pattern Recognit 44(2):295–306
Cheung R, Eisenstein B (1978) Feature selection via dynamic programming for text-independent speaker identification. IEEE Trans Acoust Speech Signal Process 26(5):397–403
Cheung Ym (2004) A rival penalized EM algorithm towards maximizing weighted likelihood for density mixture clustering with automatic model selection. In: Proceedings of the 17th international conference on Pattern recognition, 2004. ICPR 2004, vol 4. IEEE, pp 633–636
Cheung Ym (2005) Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection. IEEE Trans Knowl Data Eng 17(6):750–761
Consonni G, Marin JM (2007) Mean-field variational approximate Bayesian inference for latent variable models. Comput Stat Data Anal 52(2):790–798
Constantinopoulos C, Titsias MK, Likas A (2006) Bayesian feature and model selection for Gaussian mixture models. IEEE Trans Pattern Anal Mach Intell 28(6):1013–1018
Corduneanu A, Bishop CM (2001) Variational Bayesian model selection for mixture distributions. In: Artificial intelligence and statistics, vol 2001. Morgan Kaufmann Waltham, MA, pp 27–34
Cover TM, Van Campenhout JM (1977) On the possible orderings in the measurement selection problem. IEEE Trans Syst Man Cybern 7(9):657–661
Daelemans W, Hoste V, De Meulder F, Naudts B (2003) Combined optimization of feature selection and algorithm parameters in machine learning of language. In: Machine learning: ECML 2003. Springer, pp 84–95
Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(3):131–156
Dash M, Liu H, Motoda H (2000) Consistency based feature selection. In: Knowledge discovery and data mining. Current issues and new applications. Springer, pp 98–109
Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 2:224–227
Doak J (1992) An evaluation of feature selection methods and their application to computer security. University of California, Computer Science
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York
Dy JG (2008) Unsupervised feature selection. Computational methods of feature selection, pp 19–39
Dy JG, Brodley CE (2000) Feature subset selection and order identification for unsupervised learning. In: ICML, pp 247–254
Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res 5:845–889
Figueiredo MAT, Jain AK, Law MH (2003) A feature selection wrapper for mixtures. In: Perales FJ, Campilho AJC, de la Blanca NP, Sanfeliu A (eds) Pattern recognition and image analysis. IbPRIA 2003. Lecture notes in computer science, vol 2652. Springer, Berlin, pp 229–237
Figueiredo MA, Leitão JM, Jain AK (1999) On fitting mixture models. In: International workshop on energy minimization methods in computer vision and pattern recognition. Springer, pp 54–69
Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289–1305
Frühwirth-Schnatter S (2001) Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models. J Am Stat Assoc 96(453):194–209
Gales MJ (1999) Semi-tied covariance matrices for hidden Markov models. IEEE Trans Speech Audio Process 7(3):272–281
Gales MJ, Knill KM, Young SJ (1999) State-based Gaussian selection in large vocabulary continuous speech recognition using HMMs. IEEE Trans Speech Audio Process 7(2):152–161
Galimberti G, Manisi A, Soffritti G (2017) Modelling the role of variables in model-based cluster analysis. Stat Comput 1–25
Galimberti G, Montanari A, Viroli C (2009) Penalized factor mixture analysis for variable selection in clustered data. Comput Stat Data Anal 53(12):4301–4310
Godino-Llorente JI, Gomez-Vilda P, Blanco-Velasco M (2006) Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters. IEEE Trans Biomed Eng 53(10):1943–1953
Graham MW, Miller DJ (2006) Unsupervised learning of parsimonious mixtures on large spaces with integrated feature and component selection. IEEE Trans Signal Process 54(4):1289–1303
Günter S, Bunke H (2003) Fast feature selection in an HMM-based multiple classifier system for handwriting recognition. In: Joint pattern recognition symposium. Springer, pp 289–296
Guo J, Levina E, Michailidis G, Zhu J (2010) Pairwise variable selection for high-dimensional model-based clustering. Biometrics 66(3):793–804
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Jain AK, Duin RP, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37
Jasra A, Holmes C, Stephens D (2005) Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling. Stat Sci 50–67
Ji S, Krishnapuram B, Carin L (2006) Variational Bayes for continuous hidden Markov models and its application to active learning. IEEE Trans Pattern Anal Mach Intell 28(4):522–532
John GH, Kohavi R, Pfleger K (1994) Irrelevant features and the subset selection problem. In: Machine learning: proceedings of the eleventh international conference, pp 121–129
Kerroum MA, Hammouch A, Aboutajdine D (2010) Textural feature selection by joint mutual information based on Gaussian mixture model for multispectral image classification. Pattern Recognit Lett 31(10):1168–1174
Khreich W, Granger E, Miri A, Sabourin R (2012) A survey of techniques for incremental learning of HMM parameters. Inf Sci 197:105–130
Kim S, Tadesse MG, Vannucci M (2006) Variable selection in clustering via Dirichlet process mixture models. Biometrika 93(4):877–893
Kira K, Rendell LA (1992) The feature selection problem: traditional methods and a new algorithm. AAAI 2:129–134
Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1):273–324
Kononenko I (1994) Estimating attributes: analysis and extensions of Relief. In: Machine learning: ECML-94. Springer, pp 171–182
Krishnan S, Samudravijaya K, Rao P (1996) Feature selection for pattern classification with Gaussian mixture models: a new objective criterion. Pattern Recognit Lett 17(8):803–809
Law MH, Figueiredo MA, Jain AK (2004) Simultaneous feature selection and clustering using mixture models. IEEE Trans Pattern Anal Mach Intell 26(9):1154–1166
Law MH, Jain AK, Figueiredo M (2002) Feature selection in mixture-based clustering. In: Advances in neural information processing systems, pp 625–632
Li X, Bilmes J (2003) Feature pruning in likelihood evaluation of HMM-based speech recognition. In: 2003 IEEE workshop on automatic speech recognition and understanding, 2003. ASRU’03. IEEE, pp 303–308
Li X, Bilmes J (2005) Feature pruning for low-power ASR systems in clean and noisy environments. IEEE Signal Process Lett 12(7):489–492
Li Y, Dong M, Hua J (2008) Localized feature selection for clustering. Pattern Recognit Lett 29(1):10–18
Li Y, Dong M, Hua J (2009) Simultaneous localized feature selection and model detection for Gaussian mixtures. IEEE Trans Pattern Anal Mach Intell 31(5):953–960
Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502
Liu X, Chen T (2003) Video-based face recognition using adaptive hidden Markov models. In: 2003 IEEE computer society conference on computer vision and pattern recognition, 2003. Proceedings, vol 1. IEEE, pp I–340
Liu X, Gong Y, Xu W, Zhu S (2002) Document clustering with cluster refinement and model selection capabilities. In: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 191–198
Lv F, Nevatia R (2006) Recognition and segmentation of 3-d human action using HMM and multi-class adaboost. In: Computer vision–ECCV 2006. Springer, pp 359–372
MacKay DJ (1992) A practical Bayesian framework for backpropagation networks. Neural Comput 4(3):448–472
Marbac M, Sedki M (2017) Variable selection for model-based clustering using the integrated complete-data likelihood. Stat Comput 27(4):1049–1063
Maugis C, Celeux G, Martin-Magniette ML (2009) Variable selection for clustering with Gaussian mixture models. Biometrics 65(3):701–709
Maugis C, Celeux G, Martin-Magniette ML (2009) Variable selection in model-based clustering: a general variable role modeling. Comput Stat Data Anal 53(11):3872–3882
Maugis C, Michel B (2011) A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM Probab Stat 15:41–68
McGrory CA, Titterington D (2009) Variational Bayesian analysis for hidden Markov models. Aust N Z J Stat 51(2):227–244
McLachlan GJ, Peel D (2000) Mixtures of factor analyzers. In: Proceedings of the seventeenth international conference on machine learning. Morgan Kaufmann Publishers Inc, pp 599–606
Merialdo B (1988) Phonetic recognition using hidden Markov models and maximum mutual information training. In: 1988 international conference on acoustics, speech, and signal processing, 1988. ICASSP-88. IEEE, pp 111–114
Meyer C (2002) Utterance-level boosting of HMM speech recognizers. In: 2002 IEEE international conference on acoustics, speech, and signal processing (ICASSP), vol 1. IEEE, pp I–109
Minka TP (2001) Expectation propagation for approximate Bayesian inference. In: Proceedings of the seventeenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc, pp 362–369
Mitra P, Murthy C, Pal SK (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3):301–312
Molina LC, Belanche L, Nebot À (2002) Feature selection algorithms: a survey and experimental evaluation. In: 2002 IEEE international conference on data mining, 2002. ICDM 2003. Proceedings. IEEE, pp 306–313
Montero JA, Sucar LE (2004) Feature selection for visual gesture recognition using hidden Markov models. In: Proceedings of 5th international conference on computer science, 2004. ENC 2004. IEEE, pp 196–203
Murphy KP (2012) Machine learning: a probabilistic perspective. The MIT Press, Cambridge
Narendra PM, Fukunaga K (1977) A branch and bound algorithm for feature subset selection. IEEE Trans Comput 100(9):917–922
Ng AY (1998) On feature selection: learning with exponentially many irrelevant features as training examples. In: Proceedings of the fifteenth international conference on machine learning. Morgan Kaufmann Publishers Inc, pp 404–412
Nouza J (1996) Feature selection methods for hidden Markov model-based speech recognition. In: Proceedings of 13th international conference on pattern recognition vol 2, pp 186–190
Novovicová J, Pudil P, Kittler J (1996) Divergence based feature selection for multimodal class densities. IEEE Trans Pattern Anal Mach Intell 18(2):218–223
Olier I, Vellido A (2008) Advances in clustering and visualization of time series using GTM through time. Neural Netw 21(7):904–913
Paisley J, Carin L (2009) Hidden Markov models with stick-breaking priors. IEEE Trans Signal Process 57(10):3905–3917
Palaniappan R, Wissel T (2011) Considerations on strategies to improve EOG signal analysis. Int J Artif Life Res 2(3):6–21
Paliwal K (1992) Dimensionality reduction of the enhanced feature set for the HMM-based speech recognizer. Digital Signal Process 2(3):157–173
Pan W, Shen X (2007) Penalized model-based clustering with application to variable selection. J Mach Learn Res 8:1145–1164
Pan W, Shen X, Jiang A, Hebbel RP (2006) Semi-supervised learning via penalized mixture model with application to microarray sample classification. Bioinformatics 22(19):2388–2395
Pudil P, Ferri F, Novovicova J, Kittler J (1994a) Floating search methods for feature selection with nonmonotonic criterion functions. In: Proceedings of the twelveth international conference on pattern recognition, IAPR. Citeseer
Pudil P, Novovičová J, Kittler J (1994b) Floating search methods in feature selection. Pattern Recognit Lett 15(11):1119–1125
Pudil P, Novovičová J, Choakjarernwanit N, Kittler J (1995) Feature selection based on the approximation of class densities by finite mixtures of special type. Pattern Recognit 28(9):1389–1398
Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286
Raftery AE, Dean N (2006) Variable selection for model-based clustering. J Am Stat Assoc 101(473):168–178
Ribeiro PC, Santos-Victor J (2005) Human activity recognition from video: modeling, feature selection and classification architecture. In: Proceedings of international workshop on human activity recognition and modelling. Citeseer, pp 61–78
Richardson S, Green PJ (1997) On Bayesian analysis of mixtures with an unknown number of components (with discussion). J R Stat Soc Ser B (Stat Methodol) 59(4):731–792
Robert CP, Ryden T, Titterington DM (2000) Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method. J R Stat Soc Ser B (Stat Methodol) 62(1):57–75
Robnik-Šikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53(1–2):23–69
Rydén T et al (2008) EM versus Markov chain Monte Carlo for estimation of hidden Markov models: a computational perspective. Bayesian Anal 3(4):659–688
Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517
Schwenk H (1999) Using boosting to improve a hybrid HMM/neural network speech recognizer. In: 1999 IEEE international conference on acoustics, speech, and signal processing, 1999. Proceedings, vol 2. IEEE, pp 1009–1012
Scott SL (2002) Bayesian methods for hidden Markov models: recursive computing in the 21st century. J Am Stat Assoc 97(457):337–351
Scrucca L (2016) Genetic algorithms for subset selection in model-based clustering. In: Unsupervised learning algorithms. Springer, pp 55–70
Somol P, Pudil P, Kittler J (2004) Fast branch & bound algorithms for optimal feature selection. IEEE Trans Pattern Anal Mach Intell 26(7):900–912
Städler N, Mukherjee S et al (2013) Penalized estimation in high-dimensional hidden Markov models with state-specific graphical models. Ann Appl Stat 7(4):2157–2179
Steinley D, Brusco MJ (2008) Selection of variables in cluster analysis: an empirical comparison of eight procedures. Psychometrika 73(1):125–144
Swartz MD, Mo Q, Murphy ME, Lupton JR, Turner ND, Hong MY, Vannucci M (2008) Bayesian variable selection in clustering high-dimensional data with substructure. J Agric Biol Environ Stat 13(4):407–423
Tadesse MG, Sha N, Vannucci M (2005) Bayesian variable selection in clustering high-dimensional data. J Am Stat Assoc 100(470):602–617
Valente F, Wellekens C (2004) Variational Bayesian feature selection for Gaussian mixture models. In: IEEE international conference on acoustics, speech, and signal processing, 2004. Proceedings.(ICASSP’04), vol 1. IEEE, pp I–513
Vannucci M, Stingo FC (2010) Bayesian models for variable selection that incorporate biological information. Bayesian Stat 9:659–678
Vellido A (2006) Assessment of an unsupervised feature selection method for generative topographic mapping. In: International conference on artificial neural networks. Springer, pp 361–370
Vellido A, Lisboa PJ, Vicente D (2006) Robust analysis of MRS brain tumour data using t-GTM. Neurocomputing 69(7):754–768
Vellido A, Velazco J (2008) The effect of noise and sample size on an unsupervised feature selection method for manifold learning. In: IEEE international joint conference on neural networks, 2008. IJCNN 2008 (IEEE world congress on computational intelligence). IEEE, pp 522–527
Wang S, Zhu J (2008) Variable selection for model-based high-dimensional clustering and its application to microarray data. Biometrics 64(2):440–448
Wei X, Li C (2011) The Student’s \(t\) -hidden Markov model with truncated stick-breaking priors. IEEE Signal Process Lett 18(6):355–358
Windridge D, Bowden R (2005) Hidden Markov chain estimation and parameterisation via ICA-based feature-selection. Pattern Anal Appl 8(1–2):115–124
Wissel T, Pfeiffer T, Frysch R, Knight RT, Chang EF, Hinrichs H, Rieger JW, Rose G (2013) Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography. J Neural Eng 10(5):056,020
Witten DM, Tibshirani R (2010) A framework for feature selection in clustering. J Am Stat Assoc 105(490):713–726
Xie B, Pan W, Shen X (2008) Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables. Electron J Stat 2:168
Xie B, Pan W, Shen X (2008) Variable selection in penalized model-based clustering via regularization on grouped parameters. Biometrics 64(3):921–930
Xie B, Pan W, Shen X (2010) Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data. Bioinformatics 26(4):501–508
Xie L, Chang SF, Divakaran A, Sun H (2002) Structure analysis of soccer video with hidden Markov models. In: Proceedings of IEEE international conferene on acoustics, speech, and signal processing, vol 4
Yin P, Essa I, Rehg JM (2004) Asymmetrically boosted HMM for speech reading. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004, vol 2. IEEE, p II-755
Yin P, Essa I, Starner T, Rehg JM (2008) Discriminative feature selection for hidden Markov models using segmental boosting. In: IEEE international conference on acoustics, speech and signal processing, 2008. ICASSP 2008. IEEE, pp 2001–2004
Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. ICML 3:856–863
Yu SZ (2010) Hidden semi-Markov models. Artif Intell 174(2):215–243
Zeng H, Cheung YM (2009) A new feature selection method for Gaussian mixture clustering. Pattern Recognit 42(2):243–250
Zhou H, Pan W, Shen X (2009) Penalized model-based clustering with unconstrained covariance matrices. Electron J Stat 3:1473
Zhou J, Zhang XP (2008) An ICA mixture hidden Markov model for video content analysis. IEEE Trans Circuits Syst Video Technol 18(11):1576–1586
Zhu H, He Z, Leung H (2012) Simultaneous feature and model selection for continuous hidden Markov models. IEEE Signal Process Lett 19(5):279–282
Zhu K, Hong G, Wong Y (2008) A comparative study of feature selection for hidden Markov model-based micro-milling tool wear monitoring. Mach Sci Technol 12(3):348–369
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Adams, S., Beling, P.A. A survey of feature selection methods for Gaussian mixture models and hidden Markov models. Artif Intell Rev 52, 1739–1779 (2019). https://doi.org/10.1007/s10462-017-9581-3
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DOI: https://doi.org/10.1007/s10462-017-9581-3