Results 241 to 250 of about 118,657 (309)

An Improved LDA Approach

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2004
Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness.
Xiao-Yuan Jing, David Zhang 0001, Yuan Yan Tang   +2 more
openaire   +3 more sources

LDA-based deep transfer learning for fault diagnosis in industrial chemical processes

Computers and Chemical Engineering, 2020
Deep transfer network (DTN) has been widely used for classification tasks, which introduces maximum mean discrepancy (MMD) based loss function to extract similar latent features and reduce the discrepancy of distributions across the source and target ...
Yalin Wang, Dongzhe Wu, Xiaofeng Yuan
semanticscholar   +1 more source

LDA-MIG Detectors for Maritime Targets in Nonhomogeneous Sea Clutter

IEEE Transactions on Geoscience and Remote Sensing
This article deals with the problem of detecting maritime targets embedded in nonhomogeneous sea clutter, where the limited number of secondary data is available due to the heterogeneity of sea clutter. A class of linear discriminant analysis (LDA)-based
Xiaoqiang Hua, Linyu Peng, Weijian Liu, Yongqiang Cheng, Hongqiang Wang, Huafei Sun, Zhenghua Wang   +6 more
semanticscholar   +1 more source

Optimization of LDA parameters

2020 28th Signal Processing and Communications Applications Conference (SIU), 2020
The aim of topic modeling is to automatically discover topics in large collections of documents. Although it is used in many different fields, the questions of how to eliminate topic instability and how to optimize model parameters are not fully answered yet.
openaire   +1 more source

LDA Revisited

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016
Inference algorithms of latent Dirichlet allocation (LDA), either for small or big data, can be broadly categorized into expectation-maximization (EM), variational Bayes (VB) and collapsed Gibbs sampling (GS). Looking for a unified understanding of these different inference algorithms is currently an important open problem.
Jianwei Zhang, Jia Zeng, Mingxuan Yuan, Weixiong Rao, Jianfeng Yan   +4 more
openaire   +1 more source

Quality assessment of olive oils based on temperature-ramped HS-GC-IMS and sensory evaluation: Comparison of different processing approaches by LDA, kNN, and SVM.

Food Chemistry, 2019
For the first time, this study describes a HS-GC-IMS strategy for analyzing non-targeted volatile organic compounds (VOCs) profiles to distinguish between virgin olive oils of different classification.
Natalie Gerhardt, Sebastian Schwolow, S. Rohn, P. R. Pérez-Cacho, H. Galán-Soldevilla, L. Arce, P. Weller   +6 more
semanticscholar   +1 more source

Learning Regularized LDA by Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2014
As a supervised dimensionality reduction technique, linear discriminant analysis has a serious overfitting problem when the number of training samples per class is small. The main reason is that the between- and within-class scatter matrices computed from the limited number of training samples deviate greatly from the underlying ones.
Pang, Yanwei, Wang, Shuang, Yuan, Yuan
openaire   +3 more sources

Comparative Analysis of Classification Methods with PCA and LDA for Diabetes.

Current Diabetes Reviews, 2020
BACKGROUND The modern society is extremely prone to many life-threatening diseases, which can be easily controlled as well as cured if diagnosed at an early stage.
Dilip Kumar Choubey, Manish Kumar, Vaibhavi Shukla, Sudhakar Tripathi, V. Dhandhania   +4 more
semanticscholar   +1 more source

Robust Coverless Image Steganography Based on DCT and LDA Topic Classification

IEEE transactions on multimedia, 2018
In order to improve the robustness and capability of resisting image steganalysis, a novel coverless image steganography algorithm based on discrete cosine transform and latent dirichlet allocation (LDA) topic classification is proposed.
Xiang Zhang, Fei Peng, Min Long
semanticscholar   +1 more source

Resampling LDA/QR and PCA+LDA for Face Recognition

2005
Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the Small Sample Size (SSS) problem in traditional LDA. When applied to face recognition under varying lighting conditions and different facial expressions, neither method may work robustly.
Jun Liu 0003, Songcan Chen
openaire   +1 more source