Bilinear Discriminant Component Analysis
Bilinear Discriminant Component Analysis
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DOI：
10.5555/1314498.1314535
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发表时间：
2007-12
期刊：
影响因子：
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通讯作者：
M. Dyrholm;C. Christoforou;L. Parra
中科院分区：
文献类型：
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作者：
M. Dyrholm;C. Christoforou;L. Parra
因子分析和判别分析经常被用作互补的方法来识别二维数据阵列中的线性成分。对于可能以空间、时间和试验等维度组织数据的三维数组，有机会将这两种方法结合起来。一种新的方法，双线性判别成分分析（BDCA），在功能脑成像数据的背景下推导和演示，它似乎非常适合。该工作建议确定一个子空间投影，该子空间投影最佳地分离类，同时确保该空间中的每个维度捕获对区分的独立贡献。
Factor analysis and discriminant analysis are often used as complementary approaches to identify linear components in two dimensional data arrays. For three dimensional arrays, which may organize data in dimensions such as space, time, and trials, the opportunity arises to combine these two approaches. A new method, Bilinear Discriminant Component Analysis (BDCA), is derived and demonstrated in the context of functional brain imaging data for which it seems ideally suited. The work suggests to identify a subspace projection which optimally separates classes while ensuring that each dimension in this space captures an independent contribution to the discrimination.