Batch Processing of Differently Shaped Tensor Data

2014 Fiscal Year Final Research Report

• Project/Area Number: 23700212
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Perception information processing/Intelligent robotics
• Research Institution: Kyushu University
• Principal Investigator: INOUE Kohei 九州大学, 芸術工学研究科(研究院), 准教授 (70325570)
• Project Period (FY): 2011-04-28 – 2015-03-31
• Keywords: 画像情報処理

Outline of Final Research Achievements

For the purpose of the materialization of batch processing of differently shaped tensor data, several methods are proposed in this research includeing symmetric generalized low rank approximation of matrices, a non-iterative approximate solution method for 2DPCA, a dimensionality reduction method for vector data of different dimensions, simultaneous low rank approximation of matrices of different sizes, and simultaneous low rank approximation of matrices by nuclear norm minimization. The effectiveness of the proposed methods was experimentally demonstrated.

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