Internet of Things (IoT) terminals have been widely deployed for data sensing and analysis, and efficient data storage and transmission plays an important role in green IoT due to the explosive data growth. To simultaneously reduce the data dimension and preserves the discriminative intrinsic knowledge of data, this paper develops a novel latent discriminative low-rank projection (LDLRP) method for visual dimension reduction. Specifically, a data self-expressiveness model is established by considering the low-rank and discriminative similarity relations of data. Then, the developed model is efficiently optimized and solved via an augmented Lagrange multiplier (ALM) based-iterative algorithm, and a block-diagonal solution can be found for intraclass and interclass graph construction. Afterwards, a discriminative dimension reduced-subspace is derived by concurrently minimizing the intraclass scatter and maximizing the interclass scatter. The experimental results on benchmark datasets show that the proposed method can learn discriminative lower-dimensional expressions of high-dimensional data, and yield promising classification accuracy compared with several state-of-the-art methods. Hence, the effectiveness and efficiency of proposed method in data dimension reduction and knowledge preservation are verified, which will facilitate efficient data storage, transmission and application in green IoT.
|ジャーナル||IEEE Transactions on Green Communications and Networking|
|出版ステータス||Published - 2021 6月|
ASJC Scopus subject areas
- コンピュータ ネットワークおよび通信