Beyond Linear Subspace Clustering: A Comparative Study of Nonlinear Manifold Clustering Algorithms
| Autor: | Maryam Abdolali, Nicolas Gillis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning General Computer Science Computer science Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology computer.software_genre Field (computer science) Machine Learning (cs.LG) Theoretical Computer Science Kernel (linear algebra) FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Representation (mathematics) Linear combination Cluster analysis Artificial neural network 020206 networking & telecommunications 16. Peace & justice Linear subspace Artificial Intelligence (cs.AI) Data point ComputingMethodologies_PATTERNRECOGNITION 020201 artificial intelligence & image processing Data mining computer |
| Popis: | Subspace clustering is an important unsupervised clustering approach. It is based on the assumption that the high-dimensional data points are approximately distributed around several low-dimensional linear subspaces. The majority of the prominent subspace clustering algorithms rely on the representation of the data points as linear combinations of other data points, which is known as a self-expressive representation. To overcome the restrictive linearity assumption, numerous nonlinear approaches were proposed to extend successful subspace clustering approaches to data on a union of nonlinear manifolds. In this comparative study, we provide a comprehensive overview of nonlinear subspace clustering approaches proposed in the last decade. We introduce a new taxonomy to classify the state-of-the-art approaches into three categories, namely locality preserving, kernel based, and neural network based. The major representative algorithms within each category are extensively compared on carefully designed synthetic and real-world data sets. The detailed analysis of these approaches unfolds potential research directions and unsolved challenges in this field. 55 pages |
| Databáze: | OpenAIRE |
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