Journal of Global Optimization
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Analysis of sparse representation and blind source separation
Neural Computation
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
A Variational Method for Learning Sparse and Overcomplete Representations
Neural Computation
Learning Overcomplete Representations
Neural Computation
K-EVD clustering and its applications to sparse component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Sparse representations in unions of bases
IEEE Transactions on Information Theory
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
A hierarchical procedure for the synthesis of ANFIS networks
Advances in Fuzzy Systems
Hi-index | 0.00 |
Based on eigenvalue decomposition, a novel efficient K-HPC algorithm is developed in this paper, which is easy to implement. And it enables us to detect the number of hyperplanes and helps to avoid local minima by overestimating the number of hyperplanes. A confidence index is proposed to evaluate which estimated hyperplanes are most significant and which are spurious. So we can choose those significant hyperplanes with high rank priority and remove the spurious hyperplanes according to their corresponding confidence indices. Furthermore, a multilayer clustering framework called "multilayer K-HPC" is proposed to further improve the clustering results. The K-HPC approach can be directly applied to sparse component analysis (SCA) to develop efficient SCA algorithm. Two examples including a sparse component analysis example demonstrate the proposed algorithm.