Matrix product state decomposition in machine learning and signal processing

Publication Type:
Thesis
Issue Date:
2016
Full metadata record
Files in This Item:
Filename Description Size
01front.pdf127.5 kB
Adobe PDF
02whole.pdf2.97 MB
Adobe PDF
There has been a surge of interest in the study of multidimensional arrays, known as tensors. This is due to the fact that many real-world datasets can be represented as tensors. For example, colour images are naturally third-order tensors, which include two indices (or modes) for their spatial index, and one mode for colour. Also, a colour video is a fourth-order tensor comprised of frames, which are colour images, and an additional temporal index. Traditional tools for matrix analysis does not generalise so well in tensor analysis. The main issue is that tensors prescribe a natural structure, which is destroyed when they are vectorised. Many mathematical techniques such as principal component analysis (PCA) or linear discriminant analysis (LDA) used extensively in machine learning rely on vectorised samples of data. Additionally, since tensors may often be large in dimensionality and size, vectorising these samples and applying them to PCA or LDA may not lead to the most efficient results, and the computational time of the algorithms can increase significantly. This problem is known as the so-called curse of dimensionality. Tensor decompositions and their interesting properties are needed to circumvent this problem. The Tucker (TD) or CANDECOMP/PARAFAC (CP) decompositions have been predominantly used for tensor-based machine learning and signal processing. Both utilise common factor matrices and a core tensor, which retains the dimensionality of the original tensor. A main problem with these type of decompositions is that they essentially rely on an unbalanced matricization scheme, which potentially converts a tensor to a highly unbalanced matrix, where the row size is attributed to always one mode and the column size is the product of the remaining modes. This method is not optimal for problems that rely on retaining as much correlations within the data, which is very important for tensor-based machine learning and signal processing. In this thesis, we are interested in utilising the matrix product state (MPS) decomposition. MPS has the property that it can retain much of the correlations within a tensor because it is based on a balanced matricization scheme, which consists of permutations of matrix sizes that can investigate the different correlations amongst all modes of a tensor. Several new algorithms are proposed for tensor object classification, which demonstrate an MPS-based approach as an efficient method against other tensor-based approaches. Additionally, new methods for colour image and video completion are introduced, which outperform the current state-of-the-art tensor completion algorithms.
Please use this identifier to cite or link to this item: