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Everything about Multilinear subspace learning
Approach to dimensionality reduction
Multilinear subspace learning is an approach for disentangling the causal factor of data formation and performing dimensionality reduction. The Dimensionality reduction can be performed on a data tensor that contains a collection of observations that have been vectorized, or observations that are treated as matrices and concatenated into a data tensor. Here are some examples of data tensors whose observations are vectorized or whose observations are matrices concatenated into data tensor images (2D/3D), video sequences (3D/4D), and hyperspectral cubes (3D/4D). Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Multilinear subspace learningA 5-minute orientation
- Pick up a classic book: Multilinear Subspace LearningThe most-reprinted book on the subject
- Read the landmark paper: A survey of multilinear subspace learning for tensor dataCited 314 times
- Go sideways: Multilinear principal component analysisA closely related topic
- Go sideways: Tensor (machine learning)A closely related topic
📖 Definitions
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A survey of multilinear subspace learning for tensor data
Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning
Multilinear subspace learning using handcrafted and deep features for face kinship verification in the wild
Multilinear subspace learning for Person Re-Identification based fusion of high order tensor features
Radar Point Clouds Processing for Human Activity Classification Using Convolutional Multilinear Subspace Learning
A hybrid multilinear-linear subspace learning approach for enhanced person re-identification in camera networks
Tensor-driven face recognition: Integrating super-resolution and multilinear subspace learning for low-resolution images
📚 Books worth reading
- 📕Multilinear Subspace LearningHaiping Lu · 2013
Multilinear Subspace Learning Dimensionality Reduction Of Multidimensional DataKostantinos N. Plataniotis · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Multilinear principal component analysis Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis (PCA) that is used to analyze M-way arrays,…
- Tensor (machine learning) In machine learning, the term tensor informally refers to two different concepts: (i) a way of organizing data and (ii) a multilinear (tensor) transformation…
- Higher-order singular value decomposition In multilinear algebra, the higher-order singular value decomposition (HOSVD) is a misnomer. There does not exist a single tensor decomposition that retains…
- Tensor decomposition In multilinear algebra, a tensor decomposition is any scheme for expressing a "data tensor" (M-way array) as a sequence of elementary operations acting…
- Multiway data analysis Multiway data analysis is a method of analyzing large data sets by representing a collection of observations as a multiway array, A ∈ C I 0 × I 1 × … I…
- HOSVD-based canonical form of TP functions and qLPV models Baranyi and Yam proposed the concept of M-mode SVD/HOSVD-based canonical form of TP functions and quasi-LPV system models. Szeidl et al. proved that the…
- Principal component analysis Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data…
- Dimensionality reduction Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the…