scholarly journals Graph Regularized Semi-Supervised Concept Factorization

2012 ◽  
Vol 6-7 ◽  
pp. 583-588
Author(s):  
Yu Qing Shi ◽  
Shi Qiang Du ◽  
Wei Lan Wang
Keyword(s):  
Matrix Factorization ◽  
Matrix Decomposition ◽  
Image Database ◽  
Data Representation ◽  
Face Database ◽  
Class Label ◽  
Intrinsic Geometry ◽  
Label Information ◽  
Concept Factorization ◽  
Non Negative Matrix Factorization

Concept Factorization (CF) is a new matrix decomposition technique for data representation. A modified CF algorithm called Graph Regularized Semi-supervised Concept Factorization (GRSCF) is proposed for addressing the limitations of CF and Local Consistent Concept Factorization (LCCF), which did not consider the geometric structure or the label information of the data. GRSCF preserves the intrinsic geometry of data as regularized term and use the label information as semi-supervised learning, it makes nearby samples with the same class-label are more compact, and nearby classes are separated. Compared with Non-Negative Matrix Factorization (NMF), CNMF, CF and LCCF, experiment results on ORL face database and Coil20 image database have shown that the proposed method achieves better clustering results.

scholarly journals Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Na Yu ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Juan Wang ◽  
Junliang Shang
Keyword(s):  
Feature Selection ◽  
Matrix Factorization ◽  
Gene Selection ◽  
Matrix Decomposition ◽  
Original Data ◽  
Data Representation ◽  
Abnormal Expression ◽  
Sample Points ◽  
Laplacian Regularization ◽  
Non Negative Matrix Factorization

Abstract Background As one of the most popular data representation methods, non-negative matrix decomposition (NMF) has been widely concerned in the tasks of clustering and feature selection. However, most of the previously proposed NMF-based methods do not adequately explore the hidden geometrical structure in the data. At the same time, noise and outliers are inevitably present in the data. Results To alleviate these problems, we present a novel NMF framework named robust hypergraph regularized non-negative matrix factorization (RHNMF). In particular, the hypergraph Laplacian regularization is imposed to capture the geometric information of original data. Unlike graph Laplacian regularization which captures the relationship between pairwise sample points, it captures the high-order relationship among more sample points. Moreover, the robustness of the RHNMF is enhanced by using the L2,1-norm constraint when estimating the residual. This is because the L2,1-norm is insensitive to noise and outliers. Conclusions Clustering and common abnormal expression gene (com-abnormal expression gene) selection are conducted to test the validity of the RHNMF model. Extensive experimental results on multi-view datasets reveal that our proposed model outperforms other state-of-the-art methods.

scholarly journals Diverse Non-Negative Matrix Factorization for Multiview Data Representation

2018 ◽  
Vol 48 (9) ◽  
pp. 2620-2632 ◽  
Author(s):  
Jing Wang ◽  
Feng Tian ◽  
Hongchuan Yu ◽  
Chang Hong Liu ◽  
Kun Zhan ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Data Representation ◽  
Non Negative Matrix Factorization

scholarly journals Feature Sampling Based Unsupervised Semantic Clustering for Real Web Multi-View Content

2019 ◽  
Vol 33 ◽  
pp. 102-109
Author(s):  
Xiaolong Gong ◽  
Linpeng Huang ◽  
Fuwei Wang
Keyword(s):  
Matrix Factorization ◽  
Clustering Algorithm ◽  
Iteration Process ◽  
Sampling Strategy ◽  
Matrix Decomposition ◽  
Feature Space ◽  
Graph Regularization ◽  
Semantic Clustering ◽  
Independence Criterion ◽  
Non Negative Matrix Factorization

Real web datasets are often associated with multiple views such as long and short commentaries, users preference and so on. However, with the rapid growth of user generated texts, each view of the dataset has a large feature space and leads to the computational challenge during matrix decomposition process. In this paper, we propose a novel multi-view clustering algorithm based on the non-negative matrix factorization that attempts to use feature sampling strategy in order to reduce the complexity during the iteration process. In particular, our method exploits unsupervised semantic information in the learning process to capture the intrinsic similarity through a graph regularization. Moreover, we use Hilbert Schmidt Independence Criterion (HSIC) to explore the unsupervised semantic diversity information among multi-view contents of one web item. The overall objective is to minimize the loss function of multi-view non-negative matrix factorization that combines with an intra-semantic similarity graph regularizer and an inter-semantic diversity term. Compared with some state-of-the-art methods, we demonstrate the effectiveness of our proposed method on a large real-world dataset Doucom and the other three smaller datasets.

Blind Extraction of Correlated Fault Sources Based on Constrained Non-Negative Matrix Factorization

2012 ◽  
Vol 226-228 ◽  
pp. 760-764
Author(s):  
Ning Li ◽  
Hai Ting Chen
Keyword(s):  
Matrix Factorization ◽  
Matrix Decomposition ◽  
Separation Performance ◽  
Vibration Source ◽  
Practical Performance ◽  
Blind Extraction ◽  
The Stability ◽  
The Cost ◽  
Correlated Signals ◽  
Non Negative Matrix Factorization

Blind source separation (BSS) has been successfully used to extract undetected fault vibration sources from mixed observation signals by assuming that each unknown vibration source is mutually independent. However, conventional BSS algorithms cannot address the situation in which the fault source could be partially dependent on or correlated to other sources. For this, a new matrix decomposition method, called Non-negative Matrix Factorization (NMF), is introduced to separate these partially correlated signals. In this paper, the observed temporal signals are transformed into the frequency domain to satisfy the non-negative limit of NMF. The constraint of the least correlation between the separated sources is added into the cost function of NMF to enhance the stability of NMF, and the constrained non-negative matrix factorization (CNMF) is proposed. The simulation results show that the separation performance of CNMF is superior to the common BSS algorithms and the experiment result verifies the practical performance of CNMF.

scholarly journals Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yong-Jing Hao ◽  
Ying-Lian Gao ◽  
Mi-Xiao Hou ◽  
Ling-Yun Dai ◽  
Jin-Xing Liu
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Gene Selection ◽  
Differentially Expressed Gene ◽  
Nonnegative Matrix ◽  
Intrinsic Geometry ◽  
Sample Classification ◽  
Analysis Technique ◽  
Label Information ◽  
Discriminative Function

Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of multigeometry information between data. To solve the above problem, this paper proposed a new method called Hypergraph Regularized Discriminative Nonnegative Matrix Factorization (HDNMF), which captures intrinsic geometry by constructing hypergraphs rather than simple graphs. The introduction of the hypergraph method allows high-order relationships between samples to be considered, and the introduction of label information enables the method to have discriminative effect. Both the hypergraph Laplace and the discriminative label information are utilized together to learn the projection matrix in the standard method. In addition, we offered a corresponding multiplication update solution for the optimization. Experiments indicate that the method proposed is more effective by comparing with the earlier methods.

Manifold regularized non-negative matrix factorization with label information

2016 ◽  
Vol 25 (2) ◽  
pp. 023023 ◽  
Author(s):  
Huirong Li ◽  
Jiangshe Zhang ◽  
Changpeng Wang ◽  
Junmin Liu
Keyword(s):  
Matrix Factorization ◽  
Label Information ◽  
Non Negative Matrix Factorization
Sign in / Sign up

Export Citation Format

Share Document