MWPCR: Multiscale Weighted Principal Component Regression for High-dimensional Prediction.
MWPCR: Multiscale Weighted Principal Component Regression for High-dimensional Prediction.
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DOI：
10.1080/01621459.2016.1261710
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发表时间：
2017
影响因子：
3.7
通讯作者：
Liu LY
中科院分区：
文献类型：
--
作者：
Zhu H;Shen D;Peng X;Liu LY
我们提出了一种多尺度加权主成分回归(MWPCR)框架，该框架使用具有较强空间特征(例如，平滑和相关性)的高维特征来预测结果变量，例如疾病状态。这一发展是由识别成像生物标记物推动的，这些成像生物标记物可能有助于检测、诊断、预后评估、预测治疗反应和监测疾病状态等。MWPCR可以看作是主成分分析、核方法和回归模型的一种新的结合。该算法引入不同的权值矩阵对高维特征向量进行预白化，对降维和特征提取进行矩阵分解，并利用提取的特征建立预测模型。这种权重矩阵的例子包括用于在每个位置选择各个特征的重要性分数权重矩阵和用于合并特征向量的空间模式的空间权重矩阵。为了恢复高维特征的低维结构，我们将重要度评分权重与空间权重相结合。我们通过对阿尔茨海默病神经成像计划(ADNI)数据集的广泛模拟和真实数据分析，展示了我们方法的实用性。
We propose a multiscale weighted principal component regression (MWPCR) framework for the use of high dimensional features with strong spatial features (e.g., smoothness and correlation) to predict an outcome variable, such as disease status. This development is motivated by identifying imaging biomarkers that could potentially aid detection, diagnosis, assessment of prognosis, prediction of response to treatment, and monitoring of disease status, among many others. The MWPCR can be regarded as a novel integration of principal components analysis (PCA), kernel methods, and regression models. In MWPCR, we introduce various weight matrices to prewhitten high dimensional feature vectors, perform matrix decomposition for both dimension reduction and feature extraction, and build a prediction model by using the extracted features. Examples of such weight matrices include an importance score weight matrix for the selection of individual features at each location and a spatial weight matrix for the incorporation of the spatial pattern of feature vectors. We integrate the importance score weights with the spatial weights in order to recover the low dimensional structure of high dimensional features. We demonstrate the utility of our methods through extensive simulations and real data analyses of the Alzheimer’s disease neuroimaging initiative (ADNI) data set.
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DOI：
10.1080/10618600.2014.912135
发表时间：
2015-01
期刊：
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
影响因子：
--
作者：
Guo R;Ahn M;Zhu H;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
3.7
作者：
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通讯作者：
Buja, Andreas
影响因子：
2.5
作者：
Clemmensen, Line;Hastie, Trevor;Ersboll, Bjarne
通讯作者：
Ersboll, Bjarne
影响因子：
1.5
作者：
Bickel, PJ;Levina, E
通讯作者：
Levina, E
影响因子：
5.7
作者：
Grosenick, Logan;Klingenberg, Brad;Taylor, Jonathan E.
通讯作者：
Taylor, Jonathan E.