In order to handle the both issues, in this paper, we propose a multi-view subspace clustering method named Hashing Multi-view Sparse Subspace Learning (HMSSL). HMSSL incorporates multi-view binary code learning and binary sparse subspace learning with a "thin" dictionary into a unified ...
Large-scale image clustering has attracted sustained attention in machine learning. The traditional methods based on real value representation often suffer from the data storage and calculation. To deal with these problems, the methods based on the binary representation and the multi-view learning are...
Section 2.1 describes a method employed to eliminate outliers and Section 2.2 introduces a re-ranking approach based on clustering, and a multi-feature fusion method is proposed in Section 2.3. Experiment result and analysis To evaluate the effectiveness of the methods described in Section 2, ...
Bao BK, Zhu G, Shen J, Yan S (2013) General subspace learning with corrupted training data via graph embedding. IEEE Trans Image Process 22(11):4380–4393 CrossRef Bao BK, Zhu G, Shen J, Yan S (2013) Robust image analysis with sparse representation on quantized visual features. IEEE...
In addition, Adaboost, random subspace algorithms, and random forest were used to design their model as the base classifier. Their proposed model performed better in terms of outlier detection and classification prediction for the multiclass problem, and also did better than other classical ...
Further work will enhance the rotation invariant binary based feature in the affine space so that higher robustness in the viewpoint can be achieved with a small computation cost. Acknowledgments This work was supported in part by the China Major S&T Project (No.2013ZX01033001-001-003), the ...