Spectral Embedded Adaptive Neighbors Clustering
| Autor: | Zequn Qin, Feiping Nie, Xuelong Li, Qi Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science Similarity matrix Pattern recognition 02 engineering and technology Regularization (mathematics) Manifold Spectral clustering Computer Science Applications Distribution (mathematics) Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Representation (mathematics) business Cluster analysis Software |
| Zdroj: | IEEE transactions on neural networks and learning systems. 30(4) |
| ISSN: | 2162-2388 |
| Popis: | Spectral clustering has been widely used in various aspects, especially the machine learning fields. Clustering with similarity matrix and low-dimensional representation of data is the main reason of its promising performance shown in spectral clustering. However, such similarity matrix and low-dimensional representation directly derived from input data may not always hold when the data are high dimensional and has complex distribution. First, the similarity matrix simply based on the distance measurement might not be suitable for all kinds of data. Second, the low-dimensional representation might not be able to reflect the manifold structure of the original data. In this brief, we propose a novel linear space embedded clustering method, which uses adaptive neighbors to address the above-mentioned problems. Linearity regularization is used to make the data representation a linear embedded spectral. We also use adaptive neighbors to optimize the similarity matrix and clustering results simultaneously. Extensive experimental results show promising performance compared with the other state-of-the-art algorithms. |
| Databáze: | OpenAIRE |
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