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Fingerprint The Fingerprint is created by mining the titles and abstracts of the person's research outputs and projects/funding awards to create an index of weighted terms from discipline-specific thesauri.

Clustering algorithms Engineering & Materials Science
Clustering Mathematics
Bregman Divergence Mathematics
Factorization Engineering & Materials Science
Data mining Engineering & Materials Science
Information science Engineering & Materials Science
Covariance matrix Engineering & Materials Science
Error analysis Engineering & Materials Science

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Projects 2006 2019

ecosystem function
terrestrial ecosystem
Machine Learning
development model
Statistical Models

Research Output 2002 2017

A Spectral Algorithm for Inference in Hidden semi-Markov Models

Melnyk, I. & Banerjee, A. Apr 1 2017 In : Journal of Machine Learning Research. 18, p. 1-39 39 p.

Research output: Contribution to journalArticle

Semi-Markov Model
Expectation Maximization
Latent Variable Models
Matrix Inversion
3 Citations

Enumerating all maximal biclusters in numerical datasets

Veroneze, R., Banerjee, A. & Von Zuben, F. J. Feb 10 2017 In : Information Sciences. 379, p. 288-309 22 p.

Research output: Contribution to journalArticle

Formal concept analysis
Formal Concept Analysis
Efficient Solution

High-dimensional dependency structure learning for physical processes

Golmohammadi, J., Ebert-Uphoff, I., He, S., Deng, Y. & Banerjee, A. Dec 15 2017 Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. Institute of Electrical and Electronics Engineers Inc., Vol. 2017-November, p. 883-888 6 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Linear programming
Partial differential equations
Data mining
1 Citations

Mapping local and global variability in plant trait distributions

Butler, E. E. , Datta, A. , Flores-Moreno, H. , Chen, M. , Wythers, K. R. , Fazayeli, F. , Banerjee, A. , Atkin, O. K. , Kattge, J. , Amiaud, B. , Blonder, B. , Boenisch, G. , Bond-Lamberty, B. , Brown, K. A. , Byun, C. , Campetella, G. , Cerabolini, B. E. L. , Cornelissen, J. H. C. , Craine, J. M. , Craven, D. & 32 others De Vries, F. T., Díaz, S., Domingues, T. F., Forey, E., González-Melo, A., Gross, N., Han, W., Hattingh, W. N., Hickler, T., Jansen, S., Kramer, K., Kraft, N. J. B., Kurokawa, H., Laughlin, D. C., Meir, P., Minden, V., Niinemets, Ü., Onoda, Y., Peñuelas, J., Read, Q., Sack, L., Schamp, B., Soudzilovskaia, N. A., Spasojevic, M. J., Sosinski, E., Thornton, P. E., Valladares, F., Van Bodegom, P. M., Williams, M., Wirth, C., Reich, P. B. & Schlesinger, W. H. Dec 19 2017 In : Proceedings of the National Academy of Sciences of the United States of America. 114, 51, p. E10937-E10946

Research output: Contribution to journalArticle

Plant Dispersal

Recommendation with capacity constraints

Christakopoulou, K., Kawale, J. & Banerjee, A. Nov 6 2017 CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Association for Computing Machinery, Vol. Part F131841, p. 1439-1448 10 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Knowledge management
Capacity constraints