Abstract
Recently, cross-view feature learning has been a hot topic in machine learning due to the wide applications of multiview data. Nevertheless, the distribution discrepancy between cross-views leads to the fact that instances of the different views from same class are farther than those within the same view but from different classes. To address this problem, in this paper, we develop a novel cross-view discriminative feature subspace learning method inspired by layered visual perception from human. Firstly, the proposed method utilizes a separable low-rank self-representation model to disentangle the class and view structure layers, respectively. Secondly, a local alignment is constructed with two designed graphs to guide the subspace decomposition in a pairwise way. Finally, the global discriminative constraint on distribution center in each view is designed for further alignment improvement. Extensive cross-view classification experiments on several public datasets prove that our proposed method is more effective than other existing feature learning methods.
Highlights
Under the modern technique background, there are many artificial intelligence methods inspired by nature, such as machine learning [1,2,3,4], reinforcement learning [5], and artificial immune recognition [6]
Some researches have indicated that the adaptive ability of traditional machine learning drops sharply, when the learned images have large distribution discrepancy, such as cross-view data [2]. is discrepancy means that data variance in the view space is larger than data variance in the class space
It generates that the different views become the major factor affecting recognition. erefore, we mainly focus on cross-view subspace learning to deal with the distribution discrepancy problems in this paper
Summary
Under the modern technique background, there are many artificial intelligence methods inspired by nature, such as machine learning [1,2,3,4], reinforcement learning [5], and artificial immune recognition [6]. Liu et al pointed out that the dictionary of LRR may fail when the data is insufficient To solve this problem, the latent LRR (LatLRR) was proposed to enhance subspace learning by latent information [10]. Inspired by LRR models and LDA, Li et al unified linear discriminant constraint and low-rank representation into subspace learning to enhance the learned low-dimensional feature [11]. From the perspective of multiview data structure, a supervised subspace learning method, namely, multiview discriminant analysis (MVDA), was proposed in [28] by using the discriminative information in the different views. We design two novel discriminative alignment constraints from simultaneous local and global viewpoints, which can disentangle the class and view layers and bridge the gap existent in cross-view data.
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