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PCA helps to explain the variance in data and is a common technique for dimensionality reduction in high dimensional data.
Realization of quantum permutation algorithm in high dimensional Hilbert space.
Adaptive estimation of the rank of the coefficient matrix in high dimensional multivariate response regression models.
His research often involves developing theoretical tools for learning patterns and structures in high dimensional data.
Aggarwal, C. C., Hinneburg, A. & Keim, D. A. On the surprising behavior of distance metrics in high dimensional space.
We present a new method, RUV-4, to adjust for unwanted variation in high dimensional data with negative controls.
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It also explains why dimensionality reduction always plays a pivotal role in high-dimensional pattern classification.
Should We Model X in High-Dimensional Inference?
Clindex: Clustering for Similarity Queries in High-Dimensional Spaces.
And look at this charming British scientist talking about gradient descent in high-dimensional spaces!
Increasing the dimension in high-dimensional two-photon orbital angular momentum entanglement.
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