MPCA: Multilinear Principal Component Analysis of Tensor Objects

By Haiping Lu, K. Plataniotis, Anastasios N. Venetsanopoulos. IEEE Transactions on Neural Networks, 2008

907 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/74d381155143ec50a2de65769f16e54708f886c1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Jiwen Lu
2. G. Wang
3. Yap-Peng Tan
4. Yun Raymond Fu
5. Chengcheng Jia
6. Shengli Xie
7. Andrzej S. Cichocki
8. Guoxu Zhou

In Computer Vision and Pattern Recognition

1. Jiwen Lu
2. Yun Raymond Fu
3. G. Wang
4. Liangpei Zhang
5. Jie Zhou
6. Dacheng Tao
7. Lefei Zhang
8. Chengcheng Jia

In Signal Processing

1. Jiwen Lu
2. Yap-Peng Tan
3. Andrzej S. Cichocki
4. Yun Raymond Fu
5. Chengcheng Jia
6. Guoxu Zhou
7. Haiping Lu
8. G. Wang

Citations in the corpus

• Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, methodology): “We solve this problem in an iterative manner as inspired by recent advances in high order tensor decomposition [16, 33].”
• Hyperspectral image noise reduction based on rank-1 tensor decomposition (2013, methodology): “Tensor (Lathauwer, 1997) and multilinear algebra have been receiving more and more attention and have recently been applied to both computer vision and pattern recognition (Lu et al., 2008; Shashua and Levin, 2001; Tao et al., 2007; Vasilescu and Terzopoulos, 2003).”
• Neighborhood repulsed metric learning for kinship verification (2012, methodology): “We solve this problem in an iterative manner inspired by recent advances in EM-based algorithms [13, 28].”
• CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces (2013, methodology): “For Baseline evaluation, we conducted MPCA on the database.”
• Principal Component Analysis on Spatial Data: An Overview (2013, neutral): “2004 ; Ye, Janardan, and Li 2004 ) or 3D mode (Lu, Plataniotis, and Venetsanopoulos 2008 ).”
• Accelerated Canonical Polyadic Decomposition Using Mode Reduction (2012, positive): “Moreover, we may use the high-order SVD (HOSVD) or multilinear PCA (MPCA) methods to perform dimensionality reduction and data compression as the pre-processing step [29], [30].”
• Maximum Neighborhood Margin Discriminant Projection for Classification (2014, methodology): “Among the DR techniques, principal component analysis (PCA) [2, 3], linear discriminant analysis (LDA) [3, 4], locality preserving projection (LPP) [5, 6], and their kernelized and tensorized variants [7–18] are the most representative and well-known algorithms.”
• A survey of multilinear subspace learning for tensor data (2011, methodology): “, banks, parking lots, malls, and transportation hubs such as airports and train stations [21].”
• Low-Rank Tensor Subspace Learning for RGB-D Action Recognition (2016, methodology): “MPCA [21] extracts the principal component analysis from the tensor data.”