Tensor Subspace Analysis

By Xiaofei He, Deng Cai, Partha Niyogi. 2005

406 citations recorded by the source corpus.

How researchers see this paper

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

1. Dacheng Tao
2. Deng Cai
3. Charu C. Aggarwal
4. Yun Raymond Fu
5. Dat Thanh Tran
6. Jiawei Han
7. Jianping Gou
8. Qun Li

Citations in the corpus

• Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “With advances in mathematical tools and algorithms dealing with tensor input, many multilinear discriminant techniques as well as tensor regression have been proposed for image and video classification problems such as [18], [19], [20], [21], [22], [23], [24], [25], [26], [27].”
• 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.”
• Low-Rank Tensor Subspace Learning for RGB-D Action Recognition (2016, methodology): “• TLPP : They [22] proposed a tensor-based method on the manifold, to find a common subspace for classification.”
• A survey of multilinear subspace learning for tensor data (2011, background): “To project a tensor X in a tensor space RI1 ⊗RI2 ...⊗RIN to another tensor Y in a lower-dimensional tensor space RP1 ⊗RP2 ...⊗RPN , where Pn ≤ In for all n, N projection matrices {U(n) ∈ RIn×Pn , n = 1, ..., N} are used so that [31] Y = X ×1 U(1) T ×2 U(2) T ...×N U(N) T .”
• MPCA: Multilinear Principal Component Analysis of Tensor Objects (2008, background): “An th-order tensor resides in the tensor (multilinear) space , where are the vector (linear) spaces [23].”
• Learning a Spatially Smooth Subspace for Face Recognition (2007, methodology): “Recently there have been a lot of interest in extending the ordinary vector-based subspace learning approaches to tensor space [5, 7, 13, 26, 27].”
• Multilinear Discriminant Analysis for Higher-Order Tensor Data Classification (2014, methodology): “Index Terms—Linear discriminant analysis (LDA), multilinear discriminant analysis (MDA), direct general tensor discriminant analysis (DGTDA), constrained multilinear discriminant analysis (CMDA), pattern classification, higher-order tensor Ç”
• Beyond streams and graphs: dynamic tensor analysis (2006, background): “Similar ideas have been proposed for 2nd- or 3rd order tensors [22, 30, 12].”