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Sparse Subspace Clustering for Stream Data
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DOI：10.1109/ACCESS.2021.3054767.png)
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Abstract
En 中文
In the past few years, sparse subspace clustering (SSC) has gained many studies and found wide applications. However, SSC suffers from the limitation in scalability. Furthermore, current SSC methods could hardly tackle stream data where the structure of subspaces may change along time. In this paper, we propose a novel method to extend SSC to stream data (StreamSSC). Our method is based on maintaining a small subset of representatives to characterize the structure of the underlying subspaces during stream data. StreamSSC is efficient in both computation and memory. Experimental results on both synthetic and real-world streams demonstrate the effectiveness of StreamSSC. For efficiency, StreamSSC is faster than existing online subspace clustering methods by roughly a magnitude.
Keywords:
Clustering methods
Scalability
Optimization
Clustering algorithms
Real-time systems
Memory management
Tensors
Clustering algorithms
sparse subspace clustering
stream data
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