Abstract：The coefficient matrix solved by sparse subspace clustering(SSC) is too sparse and the coefficient matrix solved by least squares regression for subspace clustering(LSR) is too dense. Aiming at these problems, subspace clustering based on collaborative representation(SCCR) is proposed. The advantages of SSC and LSR are combined. The l1 norm and the Frobenius norm are introduced into an objective function. The coefficient solved by SCCR can group the correlated data within cluster like LSR and eliminate the connection between clusters like SSC. Then, the affinity matrix constructed by this coefficient matrix is applied to spectral clustering. The experimental results demonstrate that SCCR improves the performance of clustering.
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2017, Vol. 30 
