Coordinate Descent-Based Sparse Nonnegative Matrix Factorization for Robust Cancer-Class Discovery and Microarray Data Analysis
Determining the number of clusters in high-dimensional real-life datasets and interpreting the final outcome are among the challenging problems in data science. Discovering the number of classes in cancer and microarray data plays a vital role in the treatment and diagnosis of cancers and other rela...
Saved in:
Main Author: | Melisew Tefera Belachew |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-01-01
|
Series: | Journal of Applied Mathematics |
Online Access: | http://dx.doi.org/10.1155/2021/6675829 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Sparse Deep Nonnegative Matrix Factorization
by: Zhenxing Guo, et al.
Published: (2020-03-01) -
Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network
by: Xiangguang Dai, et al.
Published: (2018-01-01) -
Smoothing gradient descent algorithm for the composite sparse optimization
by: Wei Yang, et al.
Published: (2024-11-01) -
Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis
by: Ling-Yun Dai, et al.
Published: (2020-01-01) -
Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification
by: Mingzhu Tang, et al.
Published: (2014-01-01)