Angle Principal Component Analysis

Authors: Qianqian Wang, Quanxue Gao, Xinbo Gao, Feiping Nie

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Extensive experiments on several face image databases illustrate that our method is overall superior to the other robust PCA algorithms, such as PCA, PCA-L1 greedy, PCA-L1 nongreedy and HQ-PCA.
Researcher Affiliation Academia Qianqian Wang Quanxue Gao* Xinbo Gao Feiping Nie State Key Lab. of Integrated Services Networks, Xidian University
Pseudocode Yes Algorithm 1: Angle PCA
Open Source Code No The paper does not provide any statements about releasing open-source code or links to a code repository for the methodology described.
Open Datasets Yes We validate our approach on three face databases (Extended Yale B, CMU PIE and AR) and compared it with traditional PCA [Turk and Pentland, 1991] and recently proposed robust PCA methods including PCA-L1 greedy [Kwak, 2008], PCA-L1 non-greedy [Nie et al., 2011], and HQ-PCA [He et al., 2011].
Dataset Splits No The paper describes training and testing splits for the datasets (e.g., '32 images, which include 7 noisy images, per person for training, and the remaining images for testing'). However, it does not explicitly mention or specify any separate validation dataset splits.
Hardware Specification No The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments.
Software Dependencies No The paper mentions the use of 'Matlab function' for data construction, but it does not list specific software dependencies with version numbers for the implementation or experiments.
Experiment Setup Yes In our experiments, we use 1-nearest neighbor (1NN) for classification and set the number of projection vectors from 10 to 200. ... To be fair, we set the number of projection vectors 400, and set the number of iteration 20.