基于判别稀疏性表示的不完整多视图分类

Incomplete Multi-view Classification via Discriminative and Sparse Representation

传统多视图学习通常假设样本在每个视图都是完整的,但是由于数据难以获取、设备故障、遮挡等因素,这一假设并不总能成立,而传统的多视图学习方法很难有效处理不完整多视图数据。目前,研究者们已经提出了一些不完整多视图学习的方法,但是这些方法没有充分利用样本类别信息,从而影响恢复后样本的判别性。因此,提出基于判别稀疏性表示的不完整多视图分类方法(IMVC-DSR)。具体地,该方法假设缺失样本可用少量观测样本稀疏线性表示。同时,为了充分利用类别先验信息,增加恢复后样本的判别性,该方法鼓励相同类别样本之间相互表示,降低不同类别样本之间的相互表达。此外,该方法考虑到视图之间的相关关系,引入选择算子选出不同视图的相同样本,并约束相同样本在不同视图的线性表达具有一致性。最后,在公开的五组数据集上验证了所提方法IMVC-DSR的有效性。

Generally,the traditional multi-view learning methods assume that all samples are completed in all views.However,this assumption often fails in real applications because of limited access to data,equipment malfunction,as well as occlusion and so on.Thus,it is ineffective to directly use these traditional methods for addressing incomplete multi-view data.At present,several effective incomplete learning algorithms have been proposed,but they do not make full use of label information and thus reduce the discrimination of the recovered samples.Therefore,this paper proposes an incomplete multi-view classification method via discriminative and sparse representation(IMVCDSR).Specifically,this method is based on the assumption that missing samples can be represented linearly and sparsely by a few observed samples.Meanwhile,in order to make full use of label prior information to improve the discrimination of recovered samples,this method encourages that missing samples are represented by the samples from their same classes rather than the others.Also,according to the view consistence across multi-view,this paper designs a selection operator to select the same samples from different views and meanwhile expects their linear representations are consistent with each other.Finally,experiments demonstrate the efficacy of the proposed method on five public benchmark datasets.