Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Regression: A Unified Approach for Sparse Subspace Learning
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Improve robustness of sparse PCA by L1-norm maximization
Pattern Recognition
Structured sparse linear graph embedding
Neural Networks
Robust principal component analysis with non-greedy l1-norm maximization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Robust dimensionality reduction is an important issue in processing multivariate data. Two-dimensional principal component analysis based on L1-norm (2DPCA-L1) is a recently developed technique for robust dimensionality reduction in the image domain. The basis vectors of 2DPCA-L1, however, are still dense. It is beneficial to perform a sparse modelling for the image analysis. In this paper, we propose a new dimensionality reduction method, referred to as 2DPCA-L1 with sparsity (2DPCAL1-S), which effectively combines the robustness of 2DPCA-L1 and the sparsity-inducing lasso regularization. It is a sparse variant of 2DPCA-L1 for unsupervised learning. We elaborately design an iterative algorithm to compute the basis vectors of 2DPCAL1-S. The experiments on image data sets confirm the effectiveness of the proposed approach.