Curse of dimensionalityDifferential privacyIn general, just suppressing identifiers from released micro-data is insufficient for privacy protection. It has been shown that the risk of re-identification increases with the dimensionality of the released records. Hence, sound anonymization procedures are ...
phenomenon known as “the curse of dimensionality” in statistical learning theory. We provide an overview of the curse of dimensionality in the context of digital health, demonstrate how it can negatively impact out-of-sample performance, and highlight important considerations for researchers and algo...
The numerical approximation of parametric partial differential equations D(u,y)=0 is a computational challenge when the dimension d of the parameter vector y is large, due to the so-called curse of dimensionality. It was recently shown in [1], [2] that, for a certain class of elliptic PD...
CAN LOCAL PARTICLE FILTERS BEAT THE CURSE OF DIMENSIONALITY? There is a well-documented 芒鈧 搉atural resource curse芒鈧whereby the presence of immobile natural resources leads to weaker economic performance and a d... Rebeschini, Patrick,van Handel, Ramon - The Annals of applied probability: an...
Extracting a large number of significant features increases the representative power of the feature vector and improves the query precision. However, each feature is a dimension in the representation space, consequently handling more features worsen the dimensionality curse. The problem derives from the ...
The paper shows that the dynamic programming (DP) method due to Bellman, when augmented with an optimum sensitivity analysis, provides a mathematical basis for the above decomposition and overcomes the curse of dimensionality that limited the original formulation of DP. Numerical examples are cited....
Raw machine data can be high-dimensional due to multi-sensor measurements and discontinuous due to the wide ranges of parameter variations during continuous sensor measurements. While standard dimension reduction methods such as principal component analysis are often applied to circumvent high-dimensionality...
We propose a decomposition procedure to deflate the dimensionality problem by splitting it into manageable pieces and coordinating their solution. There are two main computational advantages in the use of decomposition methods. First, the subproblems are, by definition, smaller than the original problem...
The term “blessing of dimensionality” was introduced by Kainen in 1997 [16]. The “blessing of dimensionality” considers the same effect of concentration of distances from the different point of view [17,18,19]. The concentration of distances was discovered in the foundation of statistical phy...
The curse of dimensionality causes the well-known and widely discussed problems for machine learning methods. There is a hypothesis that using the Manhattan distance and even fractional lp quasinorms (for p less than 1) can help to overcome the curse of