Manifold learning in local tangent space via extreme learning machine
Manifold learning in local tangent space via extreme learning machine
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DOI：
10.1016/j.neucom.2015.03.116
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发表时间：
2016-01
期刊：
影响因子：
6
通讯作者：
Qian Wang-;Weiguo Wang;Rui Nian;B. He;Yue Shen;Kaj-Mikael Björk;A. Lendasse
中科院分区：
文献类型：
--
作者：
Qian Wang-;Weiguo Wang;Rui Nian;B. He;Yue Shen;Kaj-Mikael Björk;A. Lendasse
在本文中，我们提出了一种快速的流形学习策略来估计潜在的几何分布，并制定了相应的数学准则的基础上极端学习机（ELM）在高维空间。采用局部切空间对齐（LTSA）方法进行流形生成，并通过ELM建立单隐层前馈网络（SLFN）模拟低维表示过程。ELM集合的方案，然后结合了个别SLFN的模型选择，其中流形正则化机制已被带入ELM，以保持局部几何结构的LTSA。已经做了一些开发来评估ELM学习中的固有表示嵌入。仿真结果表明，所开发的方法在精度和效率方面具有优异的性能。
In this paper, we propose a fast manifold learning strategy to estimate the underlying geometrical distribution and develop the relevant mathematical criterion on the basis of the extreme learning machine (ELM) in the high-dimensional space. The local tangent space alignment (LTSA) method has been used to perform the manifold production and the single hidden layer feedforward network (SLFN) is established via ELM to simulate the low-dimensional representation process. The scheme of the ELM ensemble then combines the individual SLFN for the model selection, where the manifold regularization mechanism has been brought into ELM to preserve the local geometrical structure of LTSA. Some developments have been done to evaluate the inherent representation embedding in the ELM learning. The simulation results have shown the excellent performance in the accuracy and efficiency of the developed approach.