IEEE Trans Cybern. 2016 Feb;46(2):450-61. doi: 10.1109/TCYB.2015.2403356. Epub 2015 Feb 27.
In image analysis, the images are often represented by multiple visual features (also known as multiview features), that aim to better interpret them for achieving remarkable performance of the learning. Since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. Thus, learning with all the derived views of the data could decrease the effectiveness. To address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (MVML) learning for multiview image classification, via embedding a proposed a new block-row regularizer into the MVML framework. The block-row regularizer concatenating a Frobenius norm (F-norm) regularizer and an l(2,1)-norm regularizer is designed to conduct a hierarchical feature selection, in which the F-norm regularizer is used to conduct a high-level feature selection for selecting the informative views (i.e., discarding the uninformative views) and the l(2,1)-norm regularizer is then used to conduct a low-level feature selection on the informative views. The rationale of the use of a block-row regularizer is to avoid the issue of the over-fitting (via the block-row regularizer), to remove redundant views and to preserve the natural group structures of data (via the F-norm regularizer), and to remove noisy features (the l(2,1)-norm regularizer), respectively. We further devise a computationally efficient algorithm to optimize the derived objective function and also theoretically prove the convergence of the proposed optimization method. Finally, the results on real image datasets show that the proposed method outperforms two baseline algorithms and three state-of-the-art algorithms in terms of classification performance.
在图像分析中，图像通常由多个视觉特征（也称为多视图特征）表示，旨在更好地解释它们，以实现学习的显著性能。由于每个视图上的特征提取过程是分离的，因此图像的多个视觉特征可能包括重叠、噪声和冗余。因此，学习数据的所有派生视图可能会降低效率。为了解决这个问题，本文通过在 MVML 框架中嵌入一个新的块行正则化器，同时进行层次特征选择和多视图多标签（MVML）学习，用于多视图图像分类。所设计的块行正则化器连接 Frobenius 范数（F-范数）正则化器和 l(2,1)-范数正则化器，以进行层次特征选择，其中 F-范数正则化器用于选择信息量视图（即，丢弃无信息视图），而 l(2,1)-范数正则化器用于对信息量视图进行低级特征选择。使用块行正则化器的原理是为了避免过拟合问题（通过块行正则化器），去除冗余视图，并保留数据的自然组结构（通过 F-范数正则化器），以及去除噪声特征（l(2,1)-范数正则化器）。我们进一步设计了一种计算效率高的算法来优化导出的目标函数，并从理论上证明了所提出的优化方法的收敛性。最后，在真实图像数据集上的结果表明，所提出的方法在分类性能方面优于两个基线算法和三个最先进的算法。
