Dual subspace learning via geodesic search on Stiefel manifold

被引：4
|
作者：
Liu, Lijun [1 ]
Ge, Rendong [1 ]
Meng, Jiana [2 ]
You, Guangjie [3 ]
机构：
[1] Dalian National Univ, Sch Sci, Dalian 116600, Peoples R China
[2] Dalian National Univ, Coll Comp Sci & Technol, Dalian 116600, Peoples R China
[3] Dalian National Univ, Coll Foreign Languages & Cultures, Dalian 116600, Peoples R China
来源：
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2014年 / 5卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Principal subspace analysis; Minor subspace analysis; Dual learning; Stiefel manifold; MINOR COMPONENTS EXTRACTION; PURPOSE PRINCIPAL; NEURAL NETWORKS; ALGORITHMS; FLOW;
D O I：
10.1007/s13042-013-0217-x
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Oja's principal subspace algorithm is a well-known and powerful technique for learning and tracking principal information of time series. However, Oja's algorithm is divergent when performing the task of minor subspace analysis. In the present paper, we transform Oja's algorithm into a dual learning algorithm in the sense of fulfilling principal subspace analysis as well as minor subspace analysis via geodesic search on Stiefel manifold. Also inherent stability is guaranteed for the proposed geodesic based algorithm due to the fact the weight matrix rigourously evolves on the compact Stiefel manifold. The effectiveness of the proposed algorithm is further verified in the section of numerical simulation.
引用
收藏
页码：753 / 759
页数：7
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