Support Matrix Machines
Authors: Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li
ICML 2015 | Conference 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 RICKY@SJTU.EDU.CN Yubo Xie YUBOTSE@SJTU.EDU.CN Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Zhihua Zhang ZHIHUA@SJTU.EDU.CN Institute of Data Science, Department of Computer Science and Engineering, Shanghai Jiao Tong University, China Wu-Jun Li LIWUJUN@NJU.EDU.CN 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. |