Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,243

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

A review of unsupervised feature learning and deep learning for time-series modeling

Open access
Längkvist, Martin, Karlsson, Lars, Loutfi, Amy·2014·Pattern Recognition Letters

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.patrec.2014.01.008

Cites

19 of 19 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 19 references

Research works printed but not linked: 100

  1. Agrawal, 2013, State-of-the-art in stock prediction techniques, Int. J. Adv. Res. Electr. Electron. Instrum. Eng., 2, 1360
  2. Atsalakis, 2009, Surveying stock market forecasting techniques ĺc. Part ii: Soft computing methods, Expert Syst. Appl., 36, 5932, 10.1016/j.eswa.2008.07.006 · DOI · Paper page
  3. Y. Bengio, A. Courville, P. Vincent, Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives. Technical Report, U. Montreal, 2012. Available from: <arXiv:1206.5538>. · arXiv
  4. Bengio, 1996, Input-output HMM’s for sequence processing, IEEE Trans. Neural Networks, 7, 1231, 10.1109/72.536317 · DOI · Paper page
  5. Bengio, 1994, Learning longterm dependencies with gradient descent is difficult, IEEE Trans. Neural Networks, 5, 157, 10.1109/72.279181 · DOI · Paper page
  6. Y. Bengio, L. Yao, G. Alain, P. Vincent, Generalized denoising auto-encoders as generative models. CoRR abs/1305.6663, 2013. · arXiv
  7. Bennetts, 2011, Mobile robots for localizing gas emission sources on landfill sites: is bio-inspiration the way to go?, Front. Neuroeng., 4
  8. Bhattacharya, 2008, Preemptive identification of optimum fermentation time for black tea using electronic nose, Sens. Actuators B: Chem., 131, 110, 10.1016/j.snb.2007.12.032 · DOI
  9. Bo Chen, Jo-Anne Ting, B. Marlin, N. de Freitas, Deep learning of invariant spatio-temporal features from video, in: NIPS 2010 Deep Learning and Unsupervised Feature Learning Workshop, 2010.
  10. Carmona, 2006, Analysis of saffron volatile fraction by td–gc–ms and e-nose, Eur. Food Res. Technol., 223, 96, 10.1007/s00217-005-0144-5 · DOI
  11. K. Chang, J. Jang, C. Iliopoulos, Music genre classification via compressive sampling, in: Proceedings of the 11th International Conference on Music Information Retrieval (ISMIR), 2010, pp. 387–392.
  12. Cheung, 2001, Independent component ordering in ica time series analysis, Neurocomputing, 41, 145, 10.1016/S0925-2312(00)00358-1 · DOI
  13. S. Chiappa, J. Kober, J. Peters, Using Bayesian dynamical systems for motion template libraries, in: Advances in Neural Information Processing Systems, vol. 21, 2009, 297–304.
  14. Coates, 2010, An analysis of single-layer networks in unsupervised feature learning, Engineering, 1
  15. Dahl, 2010, Phone recognition with the mean-covariance restricted Boltzmann machine, Adv. Neural Inf. Process. Syst., 23, 469
  16. Dietterich, 2002, Machine learning for sequential data: a review, 15
  17. T. Duckett, M. Axelsson, A. Saffiotti, Learning to locate an odour source with a mobile robot, in: IEEE International Conference on Robotics and Automation, 2001. Proceedings 2001 ICRA, vol. 4, 2001, pp. 4017–4022. · DOI
  18. Dutta, 2002, Bacteria classification using cyranose 320 electronic nose, Biomed. Eng., 1, 4
  19. Erhan, 2010, Why does unsupervised pre-training help deep learning?, J. Mach. Learn. Res., 11, 625
  20. Fama, 1965, The behavior of stock-market prices, J. Bus., 1, 34, 10.1086/294743 · DOI · Paper page
  21. Furui, 2004, Speech-to-text and speech-to-speech summarization of spontaneous speech, IEEE Trans. Speech Audio Process., 12, 401, 10.1109/TSA.2004.828699 · DOI
  22. Gardner, 2000, An electronic nose system to diagnose illness, Sens. Actuators B: Chem., 70, 19, 10.1016/S0925-4005(00)00548-7 · DOI · Paper page
  23. Gardner, 2000, An electronic nose system for monitoring the quality of potable water, Sens. Actuators B: Chem., 69, 336, 10.1016/S0925-4005(00)00482-2 · DOI
  24. Gärtner, 2003, A survey of kernels for structured data, SIGKDD Explor. Newslett., 5, 49, 10.1145/959242.959248 · DOI · Paper page
  25. Gers, 2000, Learning to forget: continual prediction with LSTM, Neural Comput., 12, 2451, 10.1162/089976600300015015 · DOI · Paper page
  26. Gleicher, 2000, Animation from observation: motion capture and motion editing, SIGGRAPH Comput. Graph., 33, 51, 10.1145/345370.345409 · doi:10.1145/345370.345409
  27. Grassia, 1998, Practical parameterization of rotations using the exponential map, J. Graph. Tools, 3, 29, 10.1080/10867651.1998.10487493 · DOI · Paper page
  28. R. Grosse, R. Raina, H. Kwong, A.Y. Ng, Shift-invariant sparse coding for audio classification, in: Conference on Uncertainty in Artificial Intelligence (UAI), 2007.
  29. D. Gruhl, R. Guha, R. Kumar, J. Novak, A. Tomkins, The predictive power of online chatter, in: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 2005, pp. 78–87. · DOI · Paper page
  30. Gutierrez-Osuna, 2002, Pattern analysis for machine olfaction: a review, IEEE Sens. J., 2, 189, 10.1109/JSEN.2002.800688 · DOI · Paper page
  31. P. Hamel, D. Eck, Learning features from music audio with deep belief networks, in: 11th International Society for Music Information Retrieval Conference (ISMIR), 2010.
  32. M. Henaff, K. Jarrett, K. Kavukcuoglu, Y. LeCun, Unsupervised learning of sparse features for scalable audio classification, in: Proceedings of International Symposium on Music, Information Retrieval (ISMIR’11), 2011.
  33. Hines, 1999, Electronic noses: a review of signal processing techniques, IEE Proc. Circuits Devices Syst., 146, 297, 10.1049/ip-cds:19990670 · DOI
  34. Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647 · DOI · Paper page
  35. Hinton, 2010, A practical guide to training restricted boltzmann machines, vol. 7700, 599
  36. Hsieh, 2011, Forecasting stock markets using wavelet transforms and recurrent neural networks: an integrated system based on artificial bee colony algorithm, Appl. Soft Comput., 11, 2510, 10.1016/j.asoc.2010.09.007 · doi:10.1016/j.asoc.2010.09.007
  37. Humphrey, 2013, Feature learning and deep architectures: new directions for music informatics, J. Intell. Inf. Syst., 41, 461, 10.1007/s10844-013-0248-5 · DOI
  38. Hüsken, 2003, Recurrent neural networks for time series classification, Neurocomputing, 50, 223, 10.1016/S0925-2312(01)00706-8 · DOI · Paper page
  39. Hyvärinen, 2003, Bubbles: a unifying framework for low-level statistical properties of natural image sequences, J. Opt. Soc. Am. A, 20, 1237, 10.1364/JOSAA.20.001237 · DOI
  40. Hyvärinen, 2010, Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis, NeuroImage, 49, 257, 10.1016/j.neuroimage.2009.08.028 · DOI · Paper page
  41. Hyvèarinen, 2009, vol. 39
  42. H. Kamyshanska, R. Memisevic, On autoencoder scoring, in: Proceedings of the 30th International Conference on Machine Learning (ICML-13), JMLR Workshop and Conference Proceedings, 2013, pp. 720–728.
  43. T. van Kasteren, A. Noulas, B. Kröse, Conditional random fields versus hidden markov models for activity recognition in temporal sensor data, in: Proceedings of the 14th Annual Conference of the Advanced School for Computing and Imaging (ASCI’08), The Netherlands, 2008.
  44. E. Keogh, S. Kasetty, On the need for time series data mining benchmarks: a survey and empirical demonstration, in: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, pp. 102–111. · DOI · Paper page
  45. Kim, 1998, Time-delay recurrent neural network for temporal correlations and prediction, Neurocomputing, 20, 253, 10.1016/S0925-2312(98)00018-6 · DOI
  46. Längkvist, 2013, Fast classification of meat spoilage markers using nanostructured ZnO thin films and unsupervised feature learning, Sensors, 13, 1578, 10.3390/s130201578 · DOI
  47. Längkvist, 2012, Sleep stage classification using unsupervised feature learning, Adv. Artif. Neural Syst., 2012
  48. M. Längkvist, A. Loutfi, Unsupervised feature learning for electronic nose data applied to bacteria identification in blood, in: NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.
  49. M. Längkvist, A. Loutfi, Not all signals are created equal: dynamic objective auto-encoder for multivariate data, in: NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2012.
  50. Q.V. Le, W.Y. Zou, S.Y. Yeung, A.Y. Ng, Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis, in: Computer Vision and Pattern Recognition (CVPR), 2011. · DOI · Paper page
  51. Le Roux, 2008, Representational power of restricted Boltzmann machines and deep belief networks, Neural Comput., 20, 1631, 10.1162/neco.2008.04-07-510 · DOI · Paper page
  52. Lee, 2008, Sparse deep belief net model for visual area V2, Adv. Neural Inf. Process. Syst., 20, 873
  53. H. Lee, R. Grosse, R. Ranganath, A.Y. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, in: 26th International Conference on Machine Learning, 2009. · DOI · Paper page
  54. Lee, 2009, Unsupervised feature learning for audio classification using convolutional deep belief networks, Adv. Neural Inf. Process. Syst., 22, 1096
  55. Li, 2010, Applications of artificial neural networks in financial economics: a survey, vol. 01, 211
  56. Lin, 2009, Short-term stock price prediction based on echo state networks, Expert Syst. Appl., 36, 7313, 10.1016/j.eswa.2008.09.049 · DOI · Paper page
  57. K. Markov, T. Matsui, Music genre classification using self-taught learning via sparse coding, in: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 1929–1932. · doi:10.1109/icassp.2012.6288282
  58. Martens, 2012, Training deep and recurrent neural networks with hessian-free optimization, vol. 7700
  59. J. Masci, U. Meier, D. Cireşan, J. Schmidhuber, Stacked convolutional auto-encoders for hierarchical feature extraction, in: Proceedings of the 21th International Conference on Artificial Neural Networks, vol. Part I, 2011, pp. 52–59. · DOI · Paper page
  60. Memisevic, 2007, Unsupervised learning of image transformations, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1
  61. Memisevic, 2010, Learning to represent spatial transformations with factored higher-order Boltzmann machines, Neural Comput., 22, 1473, 10.1162/neco.2010.01-09-953 · DOI · Paper page
  62. Mirowski, 2009, Dynamic factor graphs for time series modeling, Mach. Learn. Knowl. Discovery Databases, 128, 10.1007/978-3-642-04174-7_9 · DOI
  63. P. Mirowski, D. Madhavan, Y. LeCun, Time-delay neural networks and independent component analysis for eeg-based prediction of epileptic seizures propagation, in: Association for the Advancement of Artificial Intelligence Conference, 2007.
  64. Mohamed, 2012, Acoustic modeling using deep belief networks, IEEE Trans. Audio Speech Lang. Process. Arch., 20, 14, 10.1109/TASL.2011.2109382 · DOI · Paper page
  65. A. Mohamed, G. Hinton, Phone recognition using restricted Boltzmann machines, in: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010, pp. 4354–4357. · DOI
  66. J. Nam, Learning feature representations for music classification (Ph.D. thesis), Stanford University, 2012.
  67. J. Nam, J. Herrera, M. Slaney, J.O. Smith, Learning sparse feature representations for music annotation and retrieval, in: The International Society for Music Information Retrieval (ISMIR), 2012, pp. 565–570.
  68. Nanopoulos, 2001, Feature-based classification of time-series data, Int. J. Comput. Res., 10, 49
  69. J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, A.Y. Ng, Multimodal deep learning, in: Proceedings of the 28th International Conference on Machine Learning, 2011.
  70. W. Pan, L. Torresani, Unsupervised hierarchical modeling of locomotion styles, in: Proceedings of the 26th Annual International Conference on Machine Learning, 2009, pp. 785–792. · DOI
  71. E. Parris, M. Carey, Language independent gender identification, in: 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings, vol. 2, 1996, pp. 685–688. · doi:10.1109/icassp.1996.543213
  72. R. Pascanu, T. Mikolov, Y. Bengio, Understanding the exploding gradient problem, Computing Research Repository (CoRR) abs/1211.5063, 2012. · arXiv
  73. R. Raina, A. Battle, H. Lee, B. Packer, A.Y. Ng, Self-taught learning: transfer learning from unlabeled data, in: Proceedings of the 24th International Conference on Machine Learning, 2007. · DOI · Paper page
  74. M. Ranzato, G. Hinton, Modeling pixel means and covariances using factorized third-order Boltzmann machines, in: Proceedings of Computer Vision and Pattern Recognition Conference (CVPR 2010), 2010. · DOI · Paper page
  75. M. Ranzato, A. Krizhevsky, G. Hinton, Factored 3-way restricted Boltzmann machines for modeling natural images, in: Proceedings of the International Conference on Artificial Intelligence and Statistics, 2010.
  76. A. Saxe, P. Koh, Z. Chen, M. Bhand, B. Suresh, A.Y. Ng, On random weights and unsupervised feature learning, in: Proceedings of the 28th International Conference on Machine Learning, 2011.
  77. C. Schoerkhuber, A. Klapuri, Constant-q transform toolbox for music processing, in: Seventh Sound and Music Computing Conference, 2010.
  78. D. Stavens, S. Thrun, Unsupervised learning of invariant features using video, in: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 1649–1656. · DOI
  79. M. Sugiyama, H. Sawai, A. Waibel, Review of tdnn (time delay neural network) architectures for speech recognition, in: IEEE International Sympoisum on Circuits and Systems, vol. 1, 1991, pp. 582–585. · DOI
  80. I. Sutskever, Training recurrent neural networks (Ph.D. thesis), University of Toronto, 2012.
  81. Sutskever, 2008, The recurrent temporal restricted Boltzmann machine, Adv. Neural Inf. Process. Syst., 1601
  82. G. Taylor, R. Fergus, Y. LeCun, C. Bregler, Convolutional learning of spatio-temporal features, in: Proceedings European Conference on Computer Vision (ECCV’10), 2010. · DOI · Paper page
  83. G. Taylor, G. Hinton, Factored conditional restricted Boltzmann machines for modeling motion style, in: Proceedings of the 26th International Conference on Machine Learning (ICML), 2009. · DOI · Paper page
  84. G. Taylor, G.E. Hinton, S. Roweis, Modeling human motion using binary latent variables, in: Advances in Neural Information Processing Systems, 2007. · DOI · Paper page
  85. G.W. Taylor, Composable, distributed-state models for high-dimensional time series (Ph.D. thesis), Department of Computer Science, University of Toronto, 2009.
  86. Trincavelli, 2010, Direct identification of bacteria in blood culture samples using an electronic nose, IEEE Trans. Biomed. Eng., 57, 2884, 10.1109/TBME.2010.2049492 · DOI
  87. Tsai, 2010, Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches, Decis. Support Syst., 50, 258, 10.1016/j.dss.2010.08.028 · DOI · Paper page
  88. C. Tucker, Self-organizing maps for time series analysis of electromyographic data, in: International Joint Conference on Neural Networks, 1999. IJCNN ’99, 1999, pp. 3577–3580. · doi:10.1109/ijcnn.1999.836246
  89. Vembu, 2012, On time series features and kernels for machine olfaction, Sens. Actuators B: Chem., 174, 535, 10.1016/j.snb.2012.06.070 · DOI
  90. Vito, 2007, Gas concentration estimation in ternary mixtures with room temperature operating sensor array using tapped delay architectures, Sens. Actuators B: Chem., 124, 309, 10.1016/j.snb.2006.12.039 · doi:10.1016/j.snb.2006.12.039
  91. Wang, 2013, Modeling physiological data with deep belief networks, Int. J. Inf. Educ. Technol., 3
  92. J.M. Wang, D.J. Fleet, A. Hertzmann, Multi-factor Gaussian process models for style-content separation, in: International Conference of Machine Learning (ICML), 2007, pp. 975–982. · DOI
  93. Wiskott, 2002, Slow feature analysis: unsupervised learning of invariances, Neural Comput., 14, 715, 10.1162/089976602317318938 · DOI · Paper page
  94. Wulsin, 2011, Modeling electroencephalography waveforms with semi-supervised deep belief nets: faster classification and anomaly measurement, J. Neural Eng., 8, 1741, 10.1088/1741-2560/8/3/036015 · DOI · Paper page
  95. Yamazaki, 2001, Classification of vintages of wine by artificial nose using time delay neural networks, Electron. Lett., 37, 1466, 10.1049/el:20010985 · doi:10.1049/el:20010985
  96. Yang, 2006, 10 Challenging problems in data mining research, Int. J. Inf. Technol. Decis. Making, 05, 597, 10.1142/S0219622006002258 · DOI · Paper page
  97. Zampolli, 2004, An electronic nose based on solid state sensor arrays for low-cost indoor air quality monitoring applications, Sens. Actuators B: Chem., 101, 39, 10.1016/j.snb.2004.02.024 · DOI · Paper page
  98. Zhang, 2003, Improving pattern recognition of electronic nose data with time-delay neural networks, Sens. Actuators B: Chem., 96, 385, 10.1016/S0925-4005(03)00574-4 · DOI
  99. Zhu, 2008, Predicting stock index increments by neural networks: the role of trading volume under different horizons, Expert Syst. Appl., 34, 3043, 10.1016/j.eswa.2007.06.023 · DOI
  100. W.Y. Zou, A.Y. Ng, K. Yu, Unsupervised learning of visual invariance with temporal coherence, in: NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 2011.

Cited by

See all 1,243

Metadata

Authors
Längkvist, M., Karlsson, L., & Loutfi, A.
Venue
Pattern Recognition Letters
Publisher
Elsevier BV
Published
2014-06
Volume
42
Pages
11-24
ISSN
0167-8655 (ISSN-L)
OpenAlex
W2026430219
Cited by
1,243
References
19
FWCI
65.94
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A review of unsupervised feature learning and deep learning for time-series modeling. https://app.cochat.ai/papers/10.1016/j.patrec.2014.01.008
The paper
Längkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2014.01.008

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.