On The Equivalent of Low-Rank Linear Regressions and Linear Discriminant Analysis Based Regressions
By: Researcher II
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| Authors | Xiao Cai, Chris Ding, Feiping Nie, Heng Huang |
| Journal/Conference Name | The 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) |
| Paper Category | Computer Science |
| Paper Abstract | Moreover, we will propose new discriminant low-rank ridge regression and sparse low-rank regression methods. Both of them are equivalent to doing regularized regression in the regularized LDA subspace. These new regularized objectives provide better data mining results than existing low-rank regression in both theoretical and empirical validations. We evaluate our discriminant low-rank regression methods by six benchmark datasets. In all empirical results, our discriminant low-rank models consistently show better results than the corresponding full-rank methods. |
| Date of publication | 2013 |
| Code Programming Language | MATLAB |
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