On the regularization parameter selection for sparse code learning in electrical source separation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7824 LNCS, Issue , 2013, Pages 277-286

  Figueiredo, Marisa ^a   Ribeiro, Bernardete ^a   De Almeida, Ana Maria ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

Electrical Signal Disaggregation; NILM; Parameter Regularization; Sparse Coding Learning

Indexed keywords

CODES (SYMBOLS); ENERGY CONSERVATION; ENERGY UTILIZATION; PARAMETERIZATION; SEPARATION;

COMPUTATIONAL EXPERIMENT; DISAGGREGATION; ELECTRICAL ENERGY CONSUMPTION; ENERGY SAVINGS AND SUSTAINABLE DEVELOPMENT; NILM; PARAMETER REGULARIZATION; REGULARIZATION PARAMETERS; SPARSE CODING;

SOURCE SEPARATION;

EID: 84893437547     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-37213-1_29     Document Type: Conference Paper

References (11)

1. Hart, G.W.: Nonintrusive appliance load monitoring. Proc. of the IEEE 80, 1870-1891 (1992)
2. Sultanem, F.: Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transactions on Power Delivery 6, 1380-1385 (1991)
3. Zico Kolter, J., Batra, S., Ng, A.: Energy disaggregation via discriminative sparse coding. In: Lafferty, J., Williams, C.K.I., Shawe-Taylor, J., Zemel, R., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 23, pp. 1153-1161 (2010)
4. Cole, A., Albicki, A.: Algorithm for non intrusive identification of residential appliances. In: Proc. of the IEEE Intl. Symposium on Circuits and Systems, vol. 3, pp. 338-341 (1998)
5. Patel, S.N., Robertson, T., Kientz, J.A., Reynolds, M.S., Abowd, G.D.: At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award). In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 271-288. Springer, Heidelberg (2007)
6. Figueiredo, M., De Almeida, A., Ribeiro, B.: Home electrical signal disaggrega-tion for non-intrusive load monitoring (NILM) systems. Neurocomputing 96, 66-73 (2012)
7. Hoyer, P.O.: Non-negative sparse coding. In: Proc. IEEE Workshop on Neural Networks for Signal Processing, pp. 557-565 (2002)
8. Eggert, J., Korner, E.: Sparse coding and nmf. In: Proc. IEEE Intl. Joint Conf. on Neural Networks, vol. 4, pp. 2529-2533 (2004)
9. Friedman, J., Hastie, T., Ḧofling, H., Tibshirani, R.: Pathwise coordinate optimization. The Annals of Applied Statistics 1(2), 302-332 (2007)
10. Friedman, J., Hastie, T., Tibshirani, R.: A note on the group lasso and a sparse group lasso. Technical report, Stanford University (2010)
11. Kolter, J.Z., Johnson, M.J.: REDD: A Public Data Set for Energy Disaggrega-tion Research. In: Proc. of SustKDD Workshop on Data Mining Applications in Sustainability (2011)