… dence tests under the high dimensional scenario. We show that … high dimension. Under the
assumption that the components within each high dimensional vector are weakly dependent, …

… In the HDLSS setting, we show that the proposed population dependence metric behaves
as an aggregation of group-wise (generalized) distance covariances. We construct an …

… High-dimensional Metrics (hdm) is introduced. It is a collection of statistical methods for
estimation and quantification of uncertainty in high-dimensional … Moreover, the dependence …

… problem of meaningfulness in high dimensionality is sensitive to the … metric L (1 norm) is
consistently more preferable than the Euclidean distance metric L (2 norm) for high dimensional …

… dependence structure of the design matrix might be taken into consideration for calculation
of the penalization parameter with X.dependent.… is highdimensional or z is high-dimensional. …

… sponding high-dimensional (… dimension, and thus it is more effective to use the algorithms
BRUTE and 2-APPROX, by treating the high dimensional vectors as points in a general metric …

… d,(2) C p ⩽ lim d → ∞ E Dmax d p - Dmin d p d 1 / p - 1 / 2 ⩽ ( n - 1 ) C p , where p is the
Minkowski metric, C p is an arbitrary constant dependent on the data distribution, and Dmax d p …

… learning literature have failed to quantify the precise dependence on observation noise,
dimension, rank, and our features X. Consider the fact that ap ⇥ p matrix with rank d has O(dp) …

… could be addressed by requiring the user to specify a subspace (ie, subset of dimensions) for
… An alternative solution to high dimensional settings consists in reducing the dimensionality …

… In our experiments, we tried our best to apply our ODVDM to some high-dimensional datasets
[… Thus, how to applied our ODVDM to the high-dimensional datasets is the main research …