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

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 multi-class 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 non-intrusive load monitoring. Comparisons with popular techniques show that our proposed approach yields better results on benchmark datasets.