Semi-Orthogonal Multilinear PCA with Relaxed Start
Authors: Qiquan Shi, Haiping Lu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both face (2D) and gait (3D) data demonstrate that SO-MPCA-RS outperforms other competing algorithms on the whole, and the relaxed start strategy is also effective for other TVP-based PCA methods. |
| Researcher Affiliation | Academia | Qiquan Shi and Haiping Lu Department of Computer Science Hong Kong Baptist University, Hong Kong, China |
| Pseudocode | Yes | Algorithm 1 Semi-Orthogonal Multilinear PCA with Relaxed Start (SO-MPCA-RS) |
| Open Source Code | Yes | Matlab code is available at: http://www.comp.hkbu.edu.hk/ haiping/codedata.html |
| Open Datasets | Yes | For second-order tensors, we use the same subset of the FERET database [Phillips et al., 2000] as in [Lu et al., 2009], with 721 face images from 70 subjects. For third-order tensors, we use a subset of the USF Human ID Gait Challenge database [Sarkar et al., 2005]. Both face and gait data are downloaded from: http://www.dsp. utoronto.ca/ haiping/MSL.html |
| Dataset Splits | Yes | In face recognition experiments, we randomly select L = 1, 2, 3, 4, 5, 6, 7 samples from each subject as the training data and use the rest for testing. We repeat such random splits (repetitions) ten times and report the mean correct recognition rates. In gait recognition experiments, we follow the standard setting and use the gallery set as the training data and probes A, B, and C as the test data (so there is no random splits/repetitions)... |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions "Matlab code is available at: http://www.comp.hkbu.edu.hk/ haiping/codedata.html" but does not specify the version of Matlab or any other software dependencies with their version numbers. |
| Experiment Setup | Yes | For iterative algorithms, we set the number of iterations to 20. All features are sorted according to the scatters (captured variance) in descending order for classification. We use the Nearest Neighbor Classifier with the Euclidean distance measure to classify the top P features. |