With the advances in data acquisition and storage technology, big data are being generated on a daily basis in a wide range of emerging applications. Most of these big data are multidimensional. Moreover, they are usually very-high-dimensional, with a large amount of redundancy, and only occupying a part of the input space. Therefore, dimensionality reduction is frequently employed to map high-dimensional data to a low-dimensional space while retaining as much information as possible. Linear subspace learning algorithms are traditional dimensionality reduction techniques that represent input data as vectors and solve for an optimal linear mapping to a lower-dimensional space. Unfortunately, they often become inadequate when dealing with massive multidimensional data. They result in very-high-dimensional vectors, lead to the estimation of a large number of parameters. Multilinear Subspace Learning employ different types of data tensor analysis tools for dimensionality reduction. Multilinear Subspace learning can be applied to observations whose measurements were vectorized and organized into a data tensor, or whose measurements are treated as a matrix and concatenated into a tensor.
Historically, multilinear principal component analysis has been referred to as "M-mode PCA", a terminology which was coined by Peter Kroonenberg. In 2005, Vasilescu and Terzopoulos introduced the Multilinear PCA terminology as a way to better differentiate between multilinear tensor decompositions that computed 2nd order statistics associated with each data tensor modes, and subsequent work on Multilinear Independent Component Analysis that computed higher order statistics associated with each tensor mode/axis. MPCA is an extension of PCA.
A TTP is a direct projection of a high-dimensional tensor to a low-dimensional tensor of the same order, using N projection matrices for an Nth-order tensor. It can be performed in N steps with each step performing a tensor-matrix multiplication. The N steps are exchangeable. This projection is an extension of the higher-order singular value decomposition to subspace learning. Hence, its origin is traced back to the Tucker decomposition in 1960s.
A TVP is a direct projection of a high-dimensional tensor to a low-dimensional vector, which is also referred to as the rank-one projections. As TVP projects a tensor to a vector, it can be viewed as multiple projections from a tensor to a scalar. Thus, the TVP of a tensor to a P-dimensional vector consists of P projections from the tensor to a scalar. The projection from a tensor to a scalar is an elementary multilinear projection. In EMP, a tensor is projected to a point through N unit projection vectors. It is the projection of a tensor on a single line, with one projection vector in each mode. Thus, the TVP of a tensor object to a vector in a P-dimensional vector space consists of P EMPs. This projection is an extension of the canonical decomposition, also known as the parallel factors decomposition.
Typical approach in MSL
There are N sets of parameters to be solved, one in each mode. The solution to one set often depends on the other sets. Therefore, the suboptimal iterative procedure in is followed.
Initialization of the projections in each mode
For each mode, fixing the projection in all the other mode, and solve for the projection in the current mode.
Do the mode-wise optimization for a few iterations or until convergence.
This is originated from the alternating least square method for multi-way data analysis.
Pros and cons
The advantages of MSL over traditional linear subspace modeling, in common domains where the representation is naturally somewhat tensorial, are:
MSL preserves the structure and correlation that the original data had before projection, by operating on a natural tensorial representation of the multidimensional data.
MSL can learn more compact representations than its linear counterpart; in other words, it needs to estimate a much smaller number of parameters. Thus, MSL can handle big tensor data more efficiently, by performing computations on a representation with many fewer dimensions. This leads to lower demand on computational resources.
However, MSL algorithms are iterative and are not guaranteed to converge; where an MSL algorithm does converge, it may do so at a local optimum. MSL convergence problems can often be mitigated by choosing an appropriate subspace dimensionality, and by appropriate strategies for initialization, for termination, and for choosing the order in which projections are solved.
