Locality-Based Discriminant Neighborhood Embedding
Locality-Based Discriminant Neighborhood Embedding
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DOI：
10.1093/comjnl/bxs113
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发表时间：
2013-09-01
期刊：
影响因子：
1.4
通讯作者：
Yi, Zhang
中科院分区：
文献类型：
--
作者：
Gou, Jianping;Yi, Zhang
在本文中，我们开发了一种线性监督子空间学习方法，称为基于局部性的判别邻域嵌入（LDNE），它可以利用数据的底层基于子流形的结构进行分类。我们的 LDNE 方法可以同时考虑流形学习中局部性保持投影（LPP）的“局部性”和判别邻域嵌入（DNE）的“判别性”。它可以找到一种嵌入，不仅可以保留局部信息来探索同一类数据的内在子流形结构，而且可以增强不同类子流形之间的区分度。为了研究 LDNE 的性能，我们在公开数据集上将其与最先进的降维技术（例如 LPP 和 DNE）进行了比较。实验结果表明，我们的 LDNE 可以成为一种有效且稳健的分类方法。
In this article, we develop a linear supervised subspace learning method called locality-based discriminant neighborhood embedding (LDNE), which can take advantage of the underlying submanifold-based structures of the data for classification. Our LDNE method can simultaneously consider both 'locality' of locality preserving projection (LPP) and 'discrimination' of discriminant neighborhood embedding (DNE) in manifold learning. It can find an embedding that not only preserves local information to explore the intrinsic submanifold structure of data from the same class, but also enhances the discrimination among submanifolds from different classes. To investigate the performance of LDNE, we compare it with the state-of-the-art dimensionality reduction techniques such as LPP and DNE on publicly available datasets. Experimental results show that our LDNE can be an effective and robust method for classification.