Multi-Label Consistent Convolutional Transform Learning: Application to Non-Intrusive Load Monitoring

Abstract

Convolutional transform learning is an unsupervised framework we introduced recently, for feature generation based on learnt convolutions. In this work, we propose a supervised formulation for convolutional transform so as to address the multi-label classification problem. Unlike the simple multiclass classification, in multi-label problems, each sample can correspond to multiple classes simultaneously, making the problem quite challenging. We propose to make use of a label consistency penalty and develop a suitable minimization algorithm for the training step. We illustrate the performance of the developed formulation on the practical problem of nonintrusive load monitoring. Comparisons with popular techniques show that our proposed approach yields better results on benchmark datasets.