Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne)
Abstract Feature Extraction Algorithms (FEAs) aim to address the curse of dimensionality
that makes machine learning algorithms incompetent. Our study conceptually and …
that makes machine learning algorithms incompetent. Our study conceptually and …
[PDF][PDF] Dimensionality reduction: A comparative review
In recent years, a variety of nonlinear dimensionality reduction techniques have been
proposed that aim to address the limitations of traditional techniques such as PCA. The …
proposed that aim to address the limitations of traditional techniques such as PCA. The …
[PDF][PDF] Dimensionality reduction: a comparative
In recent years, a variety of nonlinear dimensionality reduction techniques have been
proposed that aim to address the limitations of traditional techniques such as PCA and …
proposed that aim to address the limitations of traditional techniques such as PCA and …
Incremental nonlinear dimensionality reduction by manifold learning
MHC Law, AK Jain - IEEE transactions on pattern analysis and …, 2006 - ieeexplore.ieee.org
Understanding the structure of multidimensional patterns, especially in unsupervised cases,
is of fundamental importance in data mining, pattern recognition, and machine learning …
is of fundamental importance in data mining, pattern recognition, and machine learning …
[PDF][PDF] An introduction to dimensionality reduction using matlab
LJP Van der Maaten - Report, 2007 - tsam-fich.wdfiles.com
Dimensionality reduction is an important task in machine learning, for it facilitates
classification, compression, and visualization of high-dimensional data by mitigating …
classification, compression, and visualization of high-dimensional data by mitigating …
The use of hybrid manifold learning and support vector machines in the prediction of business failure
F Lin, CC Yeh, MY Lee - Knowledge-Based Systems, 2011 - Elsevier
The prediction of business failure is an important and challenging issue that has served as
the impetus for many academic studies over the past three decades. This paper proposes a …
the impetus for many academic studies over the past three decades. This paper proposes a …
Defect identification method for poplar veneer based on progressive growing generated adversarial network and MASK R-CNN model
K Hu, B Wang, Y Shen, J Guan, Y Cai - BioResources, 2020 - search.proquest.com
As the main production unit of plywood, the surface defects of veneer seriously affect the
quality and grade of plywood. Therefore, a new method for identifying wood defects based …
quality and grade of plywood. Therefore, a new method for identifying wood defects based …
A fast regularity measure for surface defect detection
DM Tsai, MC Chen, WC Li, WY Chiu - Machine Vision and applications, 2012 - Springer
In this paper, we propose a fast regularity measure for defect detection in non-textured and
homogeneously textured surfaces, with specific emphasis on ill-defined subtle defects. A …
homogeneously textured surfaces, with specific emphasis on ill-defined subtle defects. A …
Dimensionality reduction techniques for visualizing morphometric data: Comparing principal component analysis to nonlinear methods
TY Du - Evolutionary Biology, 2019 - Springer
Principal component analysis (PCA) is the most widely used dimensionality reduction
technique in the biological sciences, and is commonly employed to create 2D visualizations …
technique in the biological sciences, and is commonly employed to create 2D visualizations …
[HTML][HTML] Performance evaluation of methods for integrative dimension reduction
Dimension reduction (DR) methods play an inevitable role in analyzing and visualizing high-
dimensional multi-source data. In the recent decades many variants of these methods have …
dimensional multi-source data. In the recent decades many variants of these methods have …