论文标题
来自高维生物医学数据的可解释，相似性驱动的多视图嵌入
Interpretable, similarity-driven multi-view embeddings from high-dimensional biomedical data
论文作者
论文摘要
相似性驱动的多视图线性重建（SIMLR）是一种利用模式间关系以将大型科学数据集转化为较小，更富有良好和可解释的低维空间的算法。 SIMLR贡献了一个新的目标功能，可用于识别关节信号，基于代表先前模式关系的稀疏矩阵的正则化以及允许应用于关节减少大数据矩阵的实施，每个数据矩阵可能具有数百万个条目。我们证明，SIMLR在模拟数据中的监督学习问题，多摩学癌症生存预测数据集和多种模态神经成像数据集方面超出了密切相关的方法。综上所述，结果集合表明，SIMLR可以用默认参数应用于不同模态的关节信号估计，并且可能在各种应用域中产生实际有用的结果。
Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes a novel objective function for identifying joint signal, regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices, each of which may have millions of entries. We demonstrate that SiMLR outperforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied with default parameters to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains.
