Support Matrix Machines
Authors: Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li
ICML 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on EEG and image classification data show that our model is more robust and efficient than the state-of-the-art methods. |
| Researcher Affiliation | Academia | Luo Luo EMAIL Yubo Xie EMAIL Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Zhihua Zhang EMAIL Institute of Data Science, Department of Computer Science and Engineering, Shanghai Jiao Tong University, China Wu-Jun Li EMAIL National Key Laboratory for Novel Software Technology, Collaborative Innovation Center of Novel Software Technology and Industrialization, Department of Computer Science and Technology, Nanjing University, China |
| Pseudocode | Yes | Algorithm 1 ADMM for SMM Initialize S( 1) = b S(0) Rp q, Λ( 1) = bΛ(0) Rp q, ρ > 0, t(1) = 1, η (0, 1). for k = 1, 2, 3 . . . do (W(k), b(k)) = argmin (W,b) H(W, b) tr(bΛ(k)T W) + ρ 2||W b S(k)||2 F S(k) = argmin S G(S) + tr(bΛ(k)T S) + ρ 2||W(k) S||2 F Λ(k) = bΛ(k) ρ(W(k) S(k)) c(k) = ρ 1||Λ(k) bΛ(k)||2 F + ρ||S(k) b S(k)||2 F if c(k) < ηc(k 1) then t(k+1) = 1+ 2 b S(k+1) = S(k) + t(k) 1 t(k+1) (S(k) S(k 1)) bΛ(k+1) = Λ(k) + t(k) 1 t(k+1) (Λ(k) Λ(k 1)) else t(k+1) = 1 b S(k+1) = S(k 1) bΛ(k+1) = Λ(k 1) c(k) = η 1c(k 1) end if end for |
| Open Source Code | Yes | The code is available in http://bcmi.sjtu.edu.cn/ luoluo/code/smm.zip |
| Open Datasets | Yes | The EEG alcoholism data set arises to examine EEG correlates of genetic predisposition to alcoholism...http://kdd.ics.uci.edu/databases/eeg/ eeg.html; The EEG emotion data set (Zhu et al., 2014; Zheng et al., 2014); The student face data set contains 400 photos of Stanford University medical students (Nazir et al., 2010); The INRIA person data set...http://pascal.inrialpes.fr/data/human/ |
| Dataset Splits | Yes | For each of the compared methods, we randomly sample 70% of the data set for training and the rest for testing. All the hyperparameters involved are selected via cross validation. |
| Hardware Specification | Yes | All experiments are implemented in Matlab R2011b on a workstation with Intel Xeon CPU X5675 3.06GHz (2 12 cores), 24GB RAM, and 64bit Windows Server 2008 system. |
| Software Dependencies | Yes | All experiments are implemented in Matlab R2011b |
| Experiment Setup | Yes | All the hyperparameters involved are selected via cross validation. More specifically, we select C from {1 10 3, 2 10 3, 5 10 3, 1 10 2, 2 10 2, 5 10 2 . . . , 1 103, 2 103}. For each C, we tune τ manually to make the rank of classifier matrix varied from 1 to the size of the matrix. We repeat this procedure ten times to compute the mean and standard deviation of the classification accuracy. |