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限制深度信念网络的多视图学习Restricted Deep Belief Networks for Multi-View Learning |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_kang_restricted/ |
| 主讲教师: | Yoonseop Kang |
| 开课单位: | 浦项科技大学 |
| 开课时间: | 2011-10-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Deep belief network (DBN) is a probabilistic generative model with multiple layers of hidden nodes and a layer of visible nodes, where parameterizations between layers obey harmonium or restricted Boltzmann machines (RBMs). In this paper we present restricted deep belief network (RDBN) for multi-view learning, where each layer of hidden nodes is composed of view-specific and shared hidden nodes, in order to learn individual and shared hidden spaces from multiple views of data. View-specific hidden nodes are connected to corresponding view-specific hidden nodes in the lower-layer or visible nodes involving a specific view, whereas shared hidden nodes follow inter-layer connections without restrictions as in standard DBNs. RDBN is trained using layer-wise contrastive divergence learning. Numerical experiments on synthetic and real-world datasets demonstrate the useful behavior of the RDBN, compared to the multi-wing harmonium (MWH) which is a two-layer undirected model. |
| 关 键 词: | 深信念网络; 玻尔兹曼机; 节点; 参数化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-29：yumf |
| 阅读次数: | 96 |