Local Deep-Feature Alignment for Unsupervised Dimension Reduction
Local Deep-Feature Alignment for Unsupervised Dimension Reduction
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DOI：
10.1109/tip.2018.2804218
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发表时间：
2018-05-01
影响因子：
10.6
通讯作者：
Tao, Dacheng
中科院分区：
文献类型：
--
作者：
Zhang, Jian;Yu, Jun;Tao, Dacheng
本文提出了一种用于降维的无监督深度学习框架，称为局部深度特征对齐（LDFA）。我们为每个数据样本构建邻域，并从邻域中学习局部堆叠压缩自编码器（SCAE）以提取局部深度特征。接下来，我们利用仿射变换将每个邻域的局部深度特征与全局特征对齐。此外，我们推导出一种方法，从LDFA明确映射到学习的低维子空间的新的数据样本。LDFA方法的优点是它学习数据样本集的局部和全局特征：局部SCAE捕获数据集中包含的局部特征，而全局对齐过程将邻域之间的相互依赖性编码到最终的低维特征表示中。数据可视化、聚类和分类的实验结果表明，LDFA方法与几种著名的降维技术相比具有竞争力，在深度学习中利用局部性是一个值得进一步探索的研究课题。
This paper presents an unsupervised deep-learning framework named local deep-feature alignment (LDFA) for dimension reduction. We construct neighbourhood for each data sample and learn a local stacked contractive autoencoder (SCAE) from the neighbourhood to extract the local deep features. Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features. Moreover, we derive an approach from LDFA to map explicitly a new data sample into the learned low-dimensional subspace. The advantage of the LDFA method is that it learns both local and global characteristics of the data sample set: the local SCAEs capture local characteristics contained in the data set, while the global alignment procedures encode the interdependencies between neighbourhoods into the final low-dimensional feature representations. Experimental results on data visualization, clustering, and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring.