Abstract
We present an approach for learning models that obtain accurate classification of data objects, collected in large-scale spatio-temporal domains. The model generation is structured in three phases: spatial dimension reduction, spatio-temporal features extraction, and feature selection. Novel techniques for the first two phases are presented, with two alternatives for the middle phase. We explore model generation based on the combinations of techniques from each phase. We apply the introduced methodology to data-sets from the Voltage-Sensitive Dye Imaging (VSDI) domain, where the resulting classification models successfully decode neuronal population responses in the visual cortex of behaving animals. VSDI is currently the best technique enabling simultaneous high spatial (10,000 points) and temporal (10 ms or less) resolution imaging from neuronal population in the cortex. We demonstrate that not only our approach is scalable enough to handle computationally challenging data, but it also contributes to the neuroimaging field of study with its decoding abilities. The effectiveness of our methodology is further explored on a data-set from the hurricanes domain, and a promising direction, based on the preliminary results of hurricane severity classification, is revealed.
Similar content being viewed by others
References
Boynton GM (2005) Imaging orientation selectivity: decoding conscious perception in V1. Nat Neurosci 8(5): 541–542
Chan J, Bailey J, Leckie C (2008) Discovering correlated spatio-temporal changes in evolving graphs. Knowl Inform Syst 16(1): 53–96
Chen Y, Geisler WS, Seidemann E (2006) Optimal decoding of correlated neural population responses in the primate visual cortex. Nat Neurosci 9(11): 1412–1420
Davatzikos C, Ruparel K, Fan Y, Shen DG, Acharyya M, Loughead JW, Gur RC, Langleben DD (2005) Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. Neuroimage 28(3): 663–668
Fatourechi M, Birch G, Ward R (2007) Application of a hybrid wavelet feature selection method in the design of a self-paced brain interface system. J Neuroeng Rehabil 4(1): 11
Fauvel M, Chanussot J, Benediktsson JA (2007) A joint spatial and spectral SVM’s classification of panchromatic images. In: IEEE international geoscience and remote sensing symposium (IGARSS’07) pp 1497–1500
Gandhi V, Kang JM, Shekhar S, Ju J, Kolczyk ED, Gopal S (2009) Context inclusive function evaluation: a case study with em-based multi-scale multi-granular image classification. Knowl Inform Syst 21(2): 231–247
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3): 389–422
Han J, Kamber M (2000) Data mining: concepts and techniques, chap 3. Morgan Kaufmann, Los Altos, p 121
Kamitani Y, Tong F (2006) Decoding seen and attended motion directions from activity in the human visual cortex. Curr Biol 16(11): 1096–1102
Kang U, Tsourakakis CE, Faloutsos C (2010) Pegasus: mining peta-scale graphs. Knowl Inform Syst (in press)
Knapp KR (2008) Scientific data stewardship of international satellite cloud climatology project B1 global geostationary observations. J Appl Remote Sens 2(1): 023548
Lai C, Reinders MJ, Wessels L (2006) Random subspace method for multivariate feature selection. Pattern Recogn Lett 27(10): 1067–1076
Lee H, Choi S (2003) PCA+HMM+SVM for EEG pattern classification. In: Proceedings of the 7th international symposium on signal processing and its applications, vol 1, pp 541–544
Mitchell T, Hutchinson R, Just MA, Niculescu RS, Pereira F, Wang X (2003) Classifying instantaneous cognitive states from fMRI data. In: Proceedings of the 2003 Americal medical informatics association annual symposium. p 469
Mitchell TM, Hutchinson R, Niculescu RS, Pereira F, Wang X, Just M, Newman S (2004) Learning to decode cognitive states from brain images. Mach Learn 57(1-2): 145–175
Mörchen F (2003) Time series feature extraction for data mining using DWT and DFT, technical report 33. Department of Mathematics and Computer Science, University of Marburg, Germany
Mourao-Miranda J, Friston KJ, Brammer M (2007) Dynamic discrimination analysis: a spatial-temporal SVM. NeuroImage 36(1): 88–99
Palatucci M (2007) Temporal feature selection for f MRI analysis. Working paper (unpublished)
Palatucci M, Mitchell TM (2007) Classification in very high dimensional problems with handfuls of examples. In: Principles and practice of knowledge discovery in databases (ECML/PKDD), vol 4702 of Lecture Notes in Computer Science. Springer, pp 212–223
Simpson RH, Riehl H (1981) The hurricane and its impact. Louisiana State University Press, Baton Rouge
Singh S (2000) EEG data classification with localized structural information. In: Proceedings of the 15th international conference on pattern recognition (ICPR’00). pp 2271–2274
Singh V, Miyapuram KP, Bapi RS (2007) Detection of cognitive states from fMRI data using machine learning techniques. In Proceedings of 20th international joint conference on artificial intelligence (IJCAI’07). pp 587–592
Slovin H, Arieli A, Hildesheim R, Grinvald A (2002) Long-term voltage-sensitive dye imaging reveals cortical dynamics in behaving monkeys. J Neurophysiol 88(6): 3421–3438
Song L, Smola A, Gretton A, Borgwardt KM, Bedo J (2007) Supervised feature selection via dependence estimation. In: Proceedings of the 24th international conference on machine learning (ICML’07) ACM. pp 823–830
Stopel D, Boger Z, Moskovitch R, Shahar Y, Elovici Y (2006) Improving worm detection with artificial neural networks through feature selection and temporal analysis techniques. Int J Comput Sci Eng 15: 202–208
Vlachos M, Lin J, Keogh E, Gunopulos D (2003) A wavelet-based anytime algorithm for K-Means clustering of time series. In: Workshop on clustering high dimensionality data and its applications at the 3rd SIAM international conference on data mining. pp 23–30
Wang X, Hutchinson R, Mitchell TM (2004) Training fMRI classifiers to discriminate cognitive states across multiple subjects. In: Thrun S, Saul L, Schölkopf B (eds) Advances in neural information processing systems 16. MIT Press, Cambridge
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco
Xu L-Q, Li Y (2003) Video classification using spatial-temporal features and PCA. In: Proceedings of the 2003 international conference on multimedia and expo (ICME’03), vol 3. pp III485–III488 vol 3
Yang K, Shahabi C (2005) A PCA-based kernel for kernel PCA on multivariate time series. In: Proceedings of ICDM 2005 workshop on temporal data mining: algorithms, theory and applications held in conjunction with the fifth IEEE international conference on data mining (ICDM’05). pp 149–156
Yang K, Yoon H, Shahabi C (2005) A supervised feature subset selection technique for multivariate time series. In: Proceedings of international workshop on feature selection for data mining: interfacing machine learning with statistics (FSDM) in conjunction with 2005 SIAM international conference on data mining (SDM’05). pp 92–101
Yoon H, Shahabi C (2006) Feature subset selection on multivariate time series with extremely large spatial features. In: Proceedings of the 6th IEEE international conference on data mining—workshops (ICDMW’06). IEEE Computer Society, pp 337–342
Zhang L, Samaras D, Tomasi D, Alia-Klein N, Cottone L, Leskovjan A, Volkow ND, Goldstein R (2005) Exploiting temporal information in functional magnetic resonance imaging brain data. In: Proceedings of the 8th international conference on medical image computing and computer-assisted intervention (MICCAI’05), vol 3749 of Lecture Notes in Computer Science. Springer, pp 679–687
Zhao Q, Zhang L (2007) Temporal and spatial features of single-trial EEG for brain-computer interface. Comput Intell Neurosci 2007(1): 4
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Vainer, I., Kraus, S., Kaminka, G.A. et al. Obtaining scalable and accurate classification in large-scale spatio-temporal domains. Knowl Inf Syst 29, 527–564 (2011). https://doi.org/10.1007/s10115-010-0348-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-010-0348-2