Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation

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

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 27 (NIPS 2014)
EditorsZ. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger
Place of PublicationPalais des Congrès de Montréal, Montréal, CANADA
PublisherCurran Associates Inc
Number of pages9
Publication statusPublished - 2014
EventTwenty-eighth Conference on Neural Information Processing Systems - Montreal, Canada
Duration: 8 Dec 201413 Dec 2014


ConferenceTwenty-eighth Conference on Neural Information Processing Systems
Abbreviated titleNIPS 2014
Internet address


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