Web20 de nov. de 2024 · The Area Under the ROC Curve (AUC) is a widely used performance measure for imbalanced classification arising from many application domains where high-dimensional sparse data is abundant. In such cases, each d dimensional sample has only k non-zero features with k ≪ d, and data arrives sequentially in a streaming form. … Web13 de nov. de 2009 · This overview article introduces the difficulties that arise with high-dimensional data in the context of the very familiar linear statistical model: we give a …
IJGI Free Full-Text sgdm: An R Package for Performing Sparse ...
WebWe study high-dimensional sparse estimation tasks in a robust setting where a constant fraction of the dataset is adversarially corrupted. Specifically, we focus on the fundamental problems of robust sparse mean estimation and robust sparse PCA. We give the first practically viable robust estimators for these problems. In WebLW-k-means is tested on a number of synthetic and real-life datasets and through a detailed experimental analysis, we find that the performance of the method is highly competitive against the baselines as well as the state-of-the-art procedures for center-based high-dimensional clustering, not only in terms of clustering accuracy but also with … rock creek cc
Model selection for inferential models with high dimensional data ...
Webisotropic Gaussians in high dimensions under small mean separation. If there is a sparse subset of relevant dimensions that determine the mean separation, then the sample complexity only depends on the number of relevant dimensions and mean separation, and can be achieved by a simple computationally efficient pro-cedure. Webious subspaces of massive, high dimensional datasets and Sigkdd Explorations. Volume 6, Issue 1 - Page 90 . 0.0 0.5 1.0 1.5 Dimension a (a)11ObjectsinOneUnitBin 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 ... with means 0.5 and -0.5 in dimension aand 0.5 in dimen-sionb,andstandarddeviationsof0.2. Indimensionc,these clusters have „ = 0 and ¾ = 1. Webof datasets (e.g.output of some NN) [1, 11, 24] and for NN training [14]. These approaches exploit the follow-ing Manifold Hypothesis: non-artificial datasets in high-dimensional space often lie in a neighborhood of some manifold (surface) of much smaller dimension [5]. The paper is devoted to the problem of estimating the dimension of this ... rock creek cda