Nonnegative blind source separation by sparse component analysis based on determinant measure
The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measu...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/22063 |
| _version_ | 1848750765563183104 |
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| author | Yang, Z. Xiang, Y. Xie, S. Ding, S. Rong, Yue |
| author_facet | Yang, Z. Xiang, Y. Xie, S. Ding, S. Rong, Yue |
| author_sort | Yang, Z. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method. |
| first_indexed | 2025-11-14T07:42:02Z |
| format | Journal Article |
| id | curtin-20.500.11937-22063 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:42:02Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-220632017-09-13T16:00:10Z Nonnegative blind source separation by sparse component analysis based on determinant measure Yang, Z. Xiang, Y. Xie, S. Ding, S. Rong, Yue determinant-based sparseness measure Blind source separation (BSS) sparse component analysis nonnegative sources The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method. 2012 Journal Article http://hdl.handle.net/20.500.11937/22063 10.1109/TNNLS.2012.2208476 IEEE fulltext |
| spellingShingle | determinant-based sparseness measure Blind source separation (BSS) sparse component analysis nonnegative sources Yang, Z. Xiang, Y. Xie, S. Ding, S. Rong, Yue Nonnegative blind source separation by sparse component analysis based on determinant measure |
| title | Nonnegative blind source separation by sparse component analysis based on determinant measure |
| title_full | Nonnegative blind source separation by sparse component analysis based on determinant measure |
| title_fullStr | Nonnegative blind source separation by sparse component analysis based on determinant measure |
| title_full_unstemmed | Nonnegative blind source separation by sparse component analysis based on determinant measure |
| title_short | Nonnegative blind source separation by sparse component analysis based on determinant measure |
| title_sort | nonnegative blind source separation by sparse component analysis based on determinant measure |
| topic | determinant-based sparseness measure Blind source separation (BSS) sparse component analysis nonnegative sources |
| url | http://hdl.handle.net/20.500.11937/22063 |