【摘 要】
：
For high-dimensional data, it is often desirable to reduee the dimensionality by projection onto a low dimensional principal subspace.However, classical PCA
【机 构】
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Department of Statistics University of Pennsylvania Philadelphia, PA 19104
【出 处】
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The Third IMS-China International Conference on Statistics a
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For high-dimensional data, it is often desirable to reduee the dimensionality by projection onto a low dimensional principal subspace.However, classical PCA usually cannot find the subspace consistently in high dimensions.In this talk, we present a new principal subspace estimation method based on iterative thresholding.For a class of spiked covariance models with sparsity constraints, it consistently, and even optimally, estimates the subspace.
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