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.
|Title of host publication||Advances in Neural Information Processing Systems 27 (NIPS 2014)|
|Editors||Z Ghahramani, M Welling, C Cortes, N D Lawrence, K Q Weinberger|
|Place of Publication||Palais des Congrès de Montréal, Montréal, CANADA|
|Publisher||Curran Associates, Inc.|
|Number of pages||9|
|Publication status||Published - 1 Jan 2014|
|Event||28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada|
Duration: 8 Dec 2014 → 13 Dec 2014
|Name||Advances in Neural Information Processing Systems|
|Conference||28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014|
|Period||8/12/14 → 13/12/14|
FingerprintDive into the research topics of 'Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation'. Together they form a unique fingerprint.
- School of Natural & Computing Sciences, Computing Science - Lecturer
- Centre for Energy Transition
- Machine Learning