Nonnegative matrix factorization for document clustering: A survey
E Hosseini-Asl, JM Zurada - Artificial Intelligence and Soft Computing: 13th …, 2014 - Springer
Abstract Nonnegative Matrix Factorization (NMF) is a popular dimension reduction
technique of clustering by extracting latent features from high-dimensional data and is …
technique of clustering by extracting latent features from high-dimensional data and is …
Recognizing faces prone to occlusions and common variations using optimal face subgraphs
An intuitive graph optimization face recognition approach called Harmony Search Oriented-
EBGM (HSO-EBGM) inspired by the classical Elastic Bunch Graph Matching (EBGM) …
EBGM (HSO-EBGM) inspired by the classical Elastic Bunch Graph Matching (EBGM) …
Nonnegative matrix factorization and its application to pattern analysis and text mining
Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce
the dimensionality of the data. This presentation compares the method with other popular …
the dimensionality of the data. This presentation compares the method with other popular …
[PDF][PDF] 基于最小CIM 准则的Farrow 结构分数时延估计
于玲， 邱天爽 - 通信学报, 2015 - infocomm-journal.com
提出了一种基于最小相关熵诱导距离(CIM) 和Farrow 结构的分数时延估计算法.
该算法具有较强的抗脉仲噪声的能力, 且所需观测数据较少, 时延估计结果精度较高 …
该算法具有较强的抗脉仲噪声的能力, 且所需观测数据较少, 时延估计结果精度较高 …
Evaluation of machine learning algorithms for automatic modulation recognition
Automatic modulation recognition (AMR) becomes more important because of usable in
advanced general-purpose communication such as cognitive radio as well as specific …
advanced general-purpose communication such as cognitive radio as well as specific …
Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.
E Hosseini-Asl - 2016 - ir.library.louisville.edu
The success of machine learning algorithms generally depends on intermediate data
representation, called features that disentangle the hidden factors of variation in data …
representation, called features that disentangle the hidden factors of variation in data …
[PDF][PDF] Multiplicative algorithm for correntropy-based nonnegative matrix factorization
E Hosseini-Asl, JM Zurada - Journal of Applied Computer …, 2013 - bibliotekanauki.pl
Nonnegative matrix factorization (NMF) is a popular dimension reduction technique used for
clustering by extracting latent features from highdimensional data and is widely used for text …
clustering by extracting latent features from highdimensional data and is widely used for text …