Kernelized Supervised Laplacian Eigenmap for Visualization and Classification of Multi-Label Data
Kernelized Supervised Laplacian Eigenmap for Visualization and Classification of Multi-Label Data
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DOI：
10.1016/j.patcog.2021.108399
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发表时间：
2021-11-06
影响因子：
8
通讯作者：
Kimura, Keigo
中科院分区：
文献类型：
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作者：
Tai, Mariko;Kudo, Mineichi;Kimura, Keigo
我们之前提出了一种用于可视化的监督拉普拉斯特征映射(SLE-ML)，它可以处理多标签数据。此外，SLE-ML可以通过单个权衡参数来控制类可分离性和局部结构之间的权衡。然而，SLE-ML不能转换新数据，也就是说，它存在“样本不足”的问题。在这篇文章中，我们证明了这个问题是可解的，也就是说，可以用具有非奇异Gram矩阵的一组线性再生核(KSLEML)来完美地模拟相同的变换。我们的实验表明，训练和测试之间的差异并不大，因此，通过为权衡参数赋值，KSLE-ML可以实现低维空间中类的高度可分离性。这提供了可分离性引导的特征提取用于分类的可能性。此外，为了优化KSLEML的性能，我们同时进行了核选择和参数选择。结果表明，参数选择比核选择更重要。实验结果表明，与几种典型算法相比，KSLE-ML在可视化和特征提取方面具有优势。(C)2021年提交人(S)。由爱思唯尔有限公司出版。这是一篇基于CC by License(http://creativecommons.org/licenses/by/4.0/)的开放获取文章
We had previously proposed a supervised Laplacian eigenmap for visualization (SLE-ML) that can handle multi-label data. In addition, SLE-ML can control the trade-off between the class separability and local structure by a single trade-off parameter. However, SLE-ML cannot transform new data, that is, it has the "out-of-sample" problem. In this paper, we show that this problem is solvable, that is, it is possible to simulate the same transformation perfectly using a set of linear sums of reproducing kernels (KSLEML) with a nonsingular Gram matrix. We experimentally showed that the difference between training and testing is not large; thus, a high separability of classes in a low-dimensional space is realizable with KSLE-ML by assigning an appropriate value to the trade-off parameter. This offers the possibility of separability-guided feature extraction for classification. In addition, to optimize the performance of KSLEML, we conducted both kernel selection and parameter selection. As a result, it is shown that parameter selection is more important than kernel selection. We experimentally demonstrated the advantage of using KSLE-ML for visualization and for feature extraction compared with a few typical algorithms. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )