Locality constrained dictionary learning for non-linear dimensionality reduction and classification
Locality constrained dictionary learning for non-linear dimensionality reduction and classification
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DOI：
10.1049/iet-cvi.2015.0482
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Lina Liu;Shiwei Ma;Ling Rui;Jian Lu
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文献类型：
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作者：
Lina Liu;Shiwei Ma;Ling Rui;Jian Lu
针对现有非线性降维方法存在的增量降维问题，提出一种基于局部约束字典学习（LCDL）的增量降维算法。在字典学习过程中，约束非线性流形上一些潜在地标的邻域大小，以保持数据集的内在局部几何特征。同时，为了提高词典的识别能力，LCDL学习结构化词典，其子词典是类特定的。然后利用稀疏编码及其重构误差进行分类。降维实验结果表明，与现有方法相比，该方法能有效解决样本外扩展和大规模数据集问题。此外，人脸，性别和对象类别分类的实验结果表明，作者的算法优于一些竞争的字典学习方法。
In view of the incremental dimensionality reduction problem of existing non-linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study. During the dictionary learning process, the neighbourhood size of some potential landmarks on a non-linear manifold is constrained to maintain the intrinsic local geometric feature of the datasets. Meanwhile, to improve the dictionary's discrimination ability, a structured dictionary is learnt by LCDL, whose sub-dictionaries are class-specific. Then sparse coding and its reconstruction errors are used for classification. The experimental results of dimensionality reduction prove that, compared with the existing methods, the proposed method can solve the out of sample extension and large-scale datasets problems efficiently. In addition, the experimental results of face, gender, and object category classification demonstrate that the authors' algorithm outperforms some competing dictionary learning methods.