Clustering Algorithm for Unsupervised Monaural Musical Sound Separation Based on Non-negative Matrix Factorization

2012 ◽  
Vol E95-A (4) ◽  
pp. 818-823 ◽  
Author(s):  
Sang Ha PARK ◽  
Seokjin LEE ◽  
Koeng-Mo SUNG
Keyword(s):  
Matrix Factorization ◽  
Clustering Algorithm ◽  
Musical Sound ◽  
Sound Separation ◽  
Non Negative Matrix Factorization

scholarly journals Percussive/harmonic sound separation by non-negative matrix factorization with smoothness/sparseness constraints

2014 ◽  
Vol 2014 (1) ◽  
Author(s):  
Francisco Jesus Canadas-Quesada ◽  
Pedro Vera-Candeas ◽  
Nicolas Ruiz-Reyes ◽  
Julio Carabias-Orti ◽  
Pablo Cabanas-Molero
Keyword(s):  
Matrix Factorization ◽  
Sound Separation ◽  
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.

scholarly journals Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

Computation ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 106
Author(s):  
Angelica Alejandra Serrano-Rubio ◽  
Guillermo B. Morales-Luna ◽  
Amilcar Meneses-Viveros
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Matrix Factorization ◽  
Clustering Algorithm ◽  
Gene Selection ◽  
Clustering Algorithms ◽  
Biological Data ◽  
Dimensional Structure ◽  
Computational Time ◽  
Non Negative Matrix Factorization

Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed. It is overwhelming the amount of biological data whose high-dimensional structure exceeds mostly current computational architectures. The computational time and memory consumption optimization actually become decisive factors in choosing clustering algorithms. We propose a clustering algorithm based on Non-negative Matrix Factorization and K-means to reduce data dimensionality but whilst preserving the biological context and prioritizing gene selection, and it is implemented within parallel GPU-based environments through the CUDA library. A well-known dataset is used in our tests and the quality of the results is measured through the Rand and Accuracy Index. The results show an increase in the acceleration of 6.22× compared to the sequential version. The algorithm is competitive in the biological datasets analysis and it is invariant with respect to the classes number and the size of the gene expression matrix.

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