Transferred Low-rank Discriminative Sub-dictionary Learning Method for Information Identification and Understanding in IoT-based Natural Language Processing

Abstract

As Internet of Things (IoT) technology progresses rapidly, there is an increasing demand for automatic identification and understanding of natural language data. However, data labeling requires large amounts of effort and cost. Most intelligent algorithms rely on the assumption of uniform distribution of data, which brings great challenge to IoT-based natural language processing. To solve this problem, this study develops a transferred low-rank discriminative sub-dictionary learning (TLDSL) method. The TLDSL method learns a shared subspace through the maximum mean discrepancy (MMD) strategy that minimizes the distribution difference of sparse coefficients between the source and target domains. By learning the common sub-dictionary of the two domains, TLDSL reveals the intrinsic connection and establishes a bridge between the two domains, thus completing the knowledge transfer. By introducing the sub-dictionary incoherence, TLDSL can avoid the atomic correlation between different sub-dictionaries. In addition, the sparse coefficients are constrained in low rank representation, which can improve the model discrimination ability while preserving the global data structure. Experiments show that the TLDSL method can be effectively performed on cross-domain text classification and handwritten digit recognition.