Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning
Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning
复制标题
DOI：
10.1007/s11265-014-0937-5
复制
发表时间：
2014-08
期刊：
影响因子：
--
通讯作者：
D. Barchiesi;Mark D. Plumbley
中科院分区：
文献类型：
--
作者：
D. Barchiesi;Mark D. Plumbley
本文提出了一种从数据中学习区分非相干子空间的方法--监督迭代投影和旋转算法(S-IPR)。我们对先前提出的迭代投影和旋转(IPR)算法在监督下的扩展进行了推导，并将其用于学习非相干子空间，这些子空间对属于不同类别的信号进行建模。我们测试我们的方法作为监督分类的特征变换，首先通过可视化来自合成数据集和来自‘虹膜’数据集的变换特征，然后通过在分类实验中使用所得到的特征。
In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data. We derives-ipras a supervised extension of our previously proposed iterative projections and rotations (ipr) algorithm for incoherent dictionary learning, and we employ it to learn incoherent sub-spaces that model signals belonging to different classes. We test our method as a feature transform for supervised classification, first by visualising transformed features from a synthetic dataset and from the ‘iris’ dataset, then by using the resulting features in a classification experiment.