基于贝叶斯塔克尔分解的无监督特征选择方法，可有效识别关键特征。
Unsupervised feature selection using Bayesian Tucker decomposition
- 利用贝叶斯塔克尔分解建模残差分布，实现无监督特征筛选
- 在合成数据、耦合映射系统和基因表达数据上均取得良好效果
- 适用于复杂高维数据，适合对特征重要性敏感的研究场景
本文提出贝叶斯塔克尔分解（BTuD），其中残差假设服从高斯分布，类似于线性回归。尽管已设计相应算法实现该方法，传统高阶正交迭代仍可生成与当前实现一致的塔克尔分解。利用所提出的BTuD，可在多种合成数据集、随机耦合强度的全局耦合映射系统以及基因表达谱中成功实现无监督特征选择。结果表明，新提出的无监督特征选择方法具有潜力。此外，基于BTuD的无监督特征选择有望与先前提出的基于塔克尔分解的方法在广泛问题中保持一致。
原文摘要 · Abstract (English)
In this paper, we proposed Bayesian Tucker decomposition (BTuD) in which residual is supposed to obey Gaussian distribution analogous to linear regression. Although we have proposed an algorithm to perform the proposed BTuD, the conventional higher-order orthogonal iteration can generate Tucker decomposition consistent with the present implementation. Using the proposed BTuD, we can perform unsupervised feature selection successfully applied to various synthetic datasets, global coupled maps with randomized coupling strength, and gene expression profiles. Thus we can conclude that our newly proposed unsupervised feature selection method is promising. In addition to this, BTuD based unsupervised FE is expected to coincide with TD based unsupervised FE that were previously proposed and successfully applied to a wide range of problems.
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