Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learningdoi:10.22266/ijies2022.0831.13MACHINE learningDEEP learningINTRUSION detection systems (Computer security)
Dimensionality reduction for density ratio estimation in high-dimensional spaces. 来自 掌桥科研 喜欢 0 阅读量: 103 作者:M Sugiyama,M Kawanabe,PL Chui 摘要: The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining ...
However, its application in JDRDL framework is seldom reported. The goal of this work is to promote high-dimensional data classification performance by considering the nonlinear structure among high-dimensional data, at both dimension reduction and dictionary learning stages. To achieve this goal, a ...
王建忠 - 高维数据几何结构及降维= Geometric Structure of High-Dimensional Data and Dimensionality Reduction : 英文 被引量: 0发表: 2011年 Exploiting Geometric Structure of High Dimensional Data for Learning : An Empirical Study In machine learning, high dimensional data generally should have a high de...
The curvature is a different way of manifold processing, where traditional dimension reduction is ineffective at preserving the neighborhood. To overcome this obstacle, we perform an "operation" on the HDM using Ricci flow before a manifold's dimension reduction. More precisely, with the Ricci flow...
visualizationquality-controlframeworkrhigh-dimensional-datadimensionality-reductionmanifold-learning UpdatedMar 21, 2023 R daleroberts/hdmedians Star71 High-dimensional medians (medoid, geometric median, etc.). Fast implementations in Python. pythonmachine-learningstatisticshigh-dimensional-datamedian ...
Our analysis reveals that a lower-dimensional representation of the RSCM equations is possible not only in the equilibrium limit, but also in the slow-manifold stage of non-equilibrium turbulence. The degree of reduction depends on the type of mean-flow deformation and state of turbulence. We ...
The growing computational demand in artificial intelligence calls for hardware solutions that are capable of in situ machine learning, where both training and inference are performed by edge computation. This not only requires extremely energy-efficient architecture (such as in-memory computing) but ...
(2011) constructed a positive-definite estimator by random dimension reduction. Tucci and Wang (2019) considered a random unitary matrix with Harr measure as an alternative random operator. In this paper, inspired by the work of random matrix theory and some practical considerations, we modify the...
3D swimming path data time-segment and dimension reduction by principal components analysis (PCA); 4. machine learning model training and behavior feature identification with that data; and 5. behavior feature evaluation with the trained model and new input data (Fig. 1). The 3D swimming path ...