Structured Latent Label Consistent Dictionary Learning for Salient Machine Faults Representation-Based Robust Classification

Abstract

This paper investigates the salient machine faults representation-based classification issue by dictionary learning. A novel structured latent label consistent dictionary learning (LLC-DL) model is proposed for joint discriminative salient representation and classification. Our LLC-DL deals with the tasks by solving one objective function that aims to minimize the structured reconstruction error, structured discriminative sparse-code error and classification error simultaneously. Also, LLC-DL decomposes given signals into a sparse reconstruction part over structured latent weighted discriminative dictionary, a salient feature extraction part and an error part fitting noise. Specifically, the dictionary is learnt atom by atom, where each dictionary atom is learnt with a latent vector that reduces the disturbance between interclass atoms. The structured coding coefficients are calculated via minimizing the reconstruction error and discriminative sparse code error simultaneously. The salient representations are learnt by embedding signals onto a projection and a robust linear classifier is then trained over the learned salient features directly so that features can be ensured to be optimal for classification, where robust l 2 , 1-norm imposed on the classifier can make the prediction results more accurate. By including a salient feature extraction term, the classification approach of LLC-DL is very efficient, since there is no need to involve an extra time-consuming sparse reconstruction process with the well-trained dictionary for each test signal. Extensive simulations versify the effectiveness of our algorithm.