Model reduction of neural network trees based on dimensionality reduction
Model reduction of neural network trees based on dimensionality reduction
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DOI：
10.1109/ijcnn.2009.5178741
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发表时间：
2009-06
期刊：
影响因子：
--
通讯作者：
H. Hayashi;Qiangfu Zhao
中科院分区：
文献类型：
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作者：
H. Hayashi;Qiangfu Zhao
神经网络树（NNTree）是一种用于机器学习的混合模型。与单模型全连接神经网络相比，NNTrees更适合于结构学习，决策速度更快。最近，我们提出了一种基于启发式分组策略的高效nn树诱导算法。在本文中，我们尝试基于降维来诱导更小的nntree。我们的目标是制造出足够紧凑的nntree，以便在VLSI芯片中实现。研究了两种降维方法。一种是主成分分析（PCA），另一种是线性判别分析（LDA）。我们在几个公共数据库上进行了实验，发现降维后得到的NNTree通常节点更少，参数更少，而性能与未降维的NNTree相当。
Neural network tree (NNTree) is a hybrid model for machine learning. Compared with single model fully connected neural networks, NNTrees are more suitable for structural learning, and faster for decision making. Recently, we proposed an efficient algorithm for inducing the NNTrees based on a heuristic grouping strategy. In this paper, we try to induce smaller NNTrees based on dimensionality reduction. The goal is to induce NNTrees that are compact enough to be implemented in a VLSI chip. Two methods are investigated for dimensionality reduction. One is the principal component analysis (PCA), and another is linear discriminant analysis (LDA). We conducted experiments on several public databases, and found that the NNTree obtained after dimensionality reduction usually has less nodes and much less parameters, while the performance is comparable with the NNTree obtained without dimensionality reduction.