Large quantities of data are gathered in many domains of life, including the medical, financial, retail, industrial and social media domains. This data must be analysed such that its properties can be extracted and the underlying system understood. It is necessary to distinguish between the quantity of data, which may be large, and the information contained in the data, which may allow it to be represented, with acceptable accuracy, by a simple model. This simple model captures fundamental properties of the system, such that it can be used for the determination of the response of the system on new (unseen) data. An example of a simple model is a low degree polynomial, but this proposal considers a sparse model, which is another example of a simple model. A sparse model of a system is a model in which the dominant input variables (predictors) that determine the output, rather than all the input variables, are identified. Genomics provides an example of a sparse model because there are about 30,000 genes in the human body, but not all genes are associated directly with cancer. It is therefore desirable to identify the genes that are most directly associated with cancer, such that treatment is focused on the dominant contributory factors, rather than factors whose role in the cause of cancer is minor.
Sparsity of the solution x of the linear algebraic equation Ax=b is imposed by regularisation in the 1norm (the lasso). This is different from regularisation in the 2norm (Tikhonov regularisation), which imposes stability on x. The lasso is not understood as well as Tikhonov regularisation because of the absence of a 1norm matrix decomposition, but fundamental properties of a regularised solution of Ax=b are independent of the norm in which the regularisation is imposed. For example, a regularised solution in both norms must be stable and the error between it and the exact solution must be small. This proposal considers these properties of a regularised solution when regularisation by the lasso is used.
Computations on sparse models are, in general, simpler and faster than computations on exact dense models, which is advantageous, and important theoretical issues that must be addressed are considered in this proposal. A sparse model is an approximation of an exact dense model and there is therefore an error associated with a sparse model. A good sparse model is a model in which this error is small, and this sparse model is accepted because this small error is balanced by the greater physical insight allowed by a sparse model. Furthermore, a sparse model must be computationally reliable such that results derived from it are numerically stable, and thus a good sparse model must have a small error and be stable. It cannot, however, be assumed that all inputs yield an approximate inputoutput relationship that is sparse, stable and has a small error. It is therefore necessary to establish the class of inputs for which these properties are, and are not, satisfied. It follows that there are many issues to be considered before a sparse model can be used with confidence of its correctness. This proposal addresses these issues and it will include theoretical results and computational experiments.
The benefits of the proposed research extend to the many areas in which a sparse model is used to model an inputoutput relationship. These applications include the medical, financial, retail, industrial and social media domains (as stated above). Apart from the computational advantages of a sparse model (stated above), the desirability of a sparse model follows from its simplicity, and it is therefore easier to obtain a physical understanding of the inputoutput relationship of the system.
