TY - GEN
T1 - Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation
AU - Zhong, Mingjun
AU - Goddard, Nigel
AU - Sutton, Charles
N1 - This work is supported by the Engineering and Physical Sciences Research Council (grant number EP/K002732/1).
PY - 2014/12
Y1 - 2014/12
N2 - Blind source separation problems are difficult because they are inherently unidentifiable, yet the entire goal is to identify meaningful sources. We introduce a way of incorporating domain knowledge into this problem, called signal aggregate constraints (SACs). SACs encourage the total signal for each of the unknown sources to be close to a specified value. This is based on the observation that the total signal often varies widely across the unknown sources, and we often have a good idea of what total values to expect. We incorporate SACs into an additive factorial hidden Markov model (AFHMM) to formulate the energy disaggregation problems where only one mixture signal is assumed to be observed. A convex quadratic program for approximate inference is employed for recovering those source signals. On a real-world energy disaggregation data set, we show that the use of SACs dramatically improves the original AFHMM, and significantly improves over a recent state-of-the art approach
AB - Blind source separation problems are difficult because they are inherently unidentifiable, yet the entire goal is to identify meaningful sources. We introduce a way of incorporating domain knowledge into this problem, called signal aggregate constraints (SACs). SACs encourage the total signal for each of the unknown sources to be close to a specified value. This is based on the observation that the total signal often varies widely across the unknown sources, and we often have a good idea of what total values to expect. We incorporate SACs into an additive factorial hidden Markov model (AFHMM) to formulate the energy disaggregation problems where only one mixture signal is assumed to be observed. A convex quadratic program for approximate inference is employed for recovering those source signals. On a real-world energy disaggregation data set, we show that the use of SACs dramatically improves the original AFHMM, and significantly improves over a recent state-of-the art approach
M3 - Published conference contribution
VL - 4
T3 - Advances in Neural Information Processing Systems
SP - 3590
EP - 3598
BT - Advances in Neural Information Processing Systems 27 (NIPS 2014)
A2 - Ghahramani, Z
A2 - Welling, M
A2 - Cortes, C
A2 - Lawrence, N D
A2 - Weinberger, K Q
PB - Curran Associates, Inc.
CY - Palais des Congrès de Montréal, Montréal, CANADA
T2 - 28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014
Y2 - 8 December 2014 through 13 December 2014
ER -