Optimizing Locally Linear Classifiers with Supervised Anchor Point Learning
Authors: Xue Mao, Zhouyu Fu, Ou Wu, Weiming Hu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our method outperforms other competitive methods which employ unsupervised anchor point learning and achieves performance on par with the kernel SVM albeit with much improved efficiency. |
| Researcher Affiliation | Academia | 1NLPR, Institute of Automation, Chinese Academy of Sciences, China 2School of Computing, Engineering & Mathematics, University of Western Sydney, Australia |
| Pseudocode | Yes | Algorithm 1 LLC-SAPL |
| Open Source Code | No | The paper does not provide any concrete access information to source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | We use ten benchmark datasets: Banana, IJCNN, SKIN, Magic04, CIFAR, RCV1, USPS, MNIST, LETTER and MNIST8m. The Banana, USPS and MNIST datasets are used in [R atsch et al., 2001] [Hull, 1994] [Le Cun et al., 1998]. The IJCNN, RCV1 and MNIST8m datasets are obtained from the Lib SVM website [Chang and Lin, 2011]. The preprocessed binary CIFAR dataset is taken from [Jose et al., 2013]. The others are available at the UCI repository [Bache and Lichman, 2013]. |
| Dataset Splits | Yes | All the datasets have been divided into training and testing sets except the Banana, SKIN, Magic04 and MNIST8m datasets. For Banana and Magic04, we randomly selected two thirds of examples for training and the rest for testing. For SKIN, we used half for training and the rest for testing. The MNIST8m dataset contains 8.1 million examples and was generated by performing careful elastic deformation of the original MNIST training set [Loosli et al., 2007]. We used the first 8 million examples as training data and tested on the 10,000 examples in the original MNIST testing set. The regularization parameter C is tuned by 5-fold cross validation on the training set for both linear and kernel SVMs. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or detailed computer specifications) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions using 'Lib Linear [Fan et al., 2008] and Lib SVM [Chang and Lin, 2011]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For the two proposed methods (LLC-SAPL and LLC-SAPLlcc), LLSVM and LCC+SVM, we adopted the same parameter values suggested in [Ladicky and Torr, 2011] by setting the number of anchor points m to 100 and number of nearest neighbours k to 8 for the local coding step. The regularization parameter C is tuned by 5-fold cross validation on the training set for both linear and kernel SVMs. Cross validation is also used to tune the kernel width of the Gaussian kernel for kernel SVM, and the number of nearest neighbors for SVM-KNN. |