Learning Linear Dynamical Systems via Spectral Filtering

We present an efficient and practical algorithm for the online prediction of discrete-time linear dynamical systems with a symmetric transition matrix. We circumvent the non-convex optimization problem using improper learning: carefully overparameterize the class of LDSs by a polylogarithmic factor, in exchange for convexity of the loss functions. From this arises a polynomial-time algorithm with a near-optimal regret guarantee, with an analogous sample complexity bound for agnostic learning. Our algorithm is based on a novel filtering technique, which may be of independent interest: we convolve the time series with the eigenvectors of a certain Hankel matrix.Comment: Published as a conference paper at NIPS 201

Sentiment analysis of ASOS product reviews using machine learning algorithms by comparing several models.

Digital ratings are crucial in improving international customer communications and impacting consumer purchasing trends. To obtain important data from a massive number of customer reviews, they must be sorted into positive and negative opinions. Sentiment analysis is a computational method for extracting emotive information from a text. In this particular research, over 3000 reviews have been obtained from the ASOS website and classified into three different sentiments: excellent, average, and bad. The obtained reviews have been pre-processed, then feature extraction is applied to the pre-processed data to remove the redundant data. Finally, distinct machine learning algorithms will be utilized to build disparate models. This research is vital as it allows the ASOS organization to gain insight into how consumers perceive about specific issues and detect urgent issues such as delivery delays and misplaced packages in the current time period before the issue goes outof control. The key results of this research show that the Nu-Support Vector Classification model obtained the highest accuracy score of 85.99% and the lowest accuracy score of 51.47% was obtained for the AdaBoost classifier model

ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes

Novel applications of machine learning in astronomy and beyond

The field of astronomy is currently experiencing a period of unprecedented expansion, predominantly brought about by the vast amounts of data being produced by the latest telescopes and surveys. New methods will be required to have any hope of being able to analyse the data collected, the most widespread of which is machine learning. Machine learning has evolved rapidly over the past decade in an attempt to match the rate of increasing data, and aided by advancements in computer hardware, analyses that would have been impossible in the past are now common place on astronomers’ laptops. However, despite machine learning becoming a favourite tool for many, there is often little consideration for which algorithms are best suited for the job. In this thesis, machine learning is implemented in a variety of different problems ranging from Solar System science and searching for Trans-Neptunian Objects (TNOs), to the cosmological problem of obtaining accurate photometric redshift (photo-z) estimations for distant galaxies. In chapter 2 I implement many different machine learning classifiers to aid the Dark Energy Survey’s search for TNOs, comparing the classifiers to find the most suitable, and demonstrating how machine learning can provide significant increases in efficiency. In chapter 3 I implement machine learning algorithms to provide photo-z estimations for a million galaxies, using the method as an example for how it is possible to benchmark machine learning algorithms to provide information about the scalibility of different methods. In chapter 4 I expand upon the benchmarking of methods developed for obtaining photo-z estimates, applying them instead to deep learning algorithms which directly use image data, before discussing future work and concluding in chapter 5

Computing different realizations of linear dynamical systems with embedding eigenvalue assignment

In this paper we investigate realizability of discrete time linear dynamical systems (LDSs) in fixed state space dimension. We examine whether there exist different Θ = (A,B,C,D) state space realizations of a given Markov parameter sequence Y with fixed B, C and D state space realization matrices. Full observation is assumed in terms of the invertibility of output mapping matrix C. We prove that the set of feasible state transition matrices associated to a Markov parameter sequence Y is convex, provided that the state space realization matrices B, C and D are known and fixed. Under the same conditions we also show that the set of feasible Metzler-type state transition matrices forms a convex subset. Regarding the set of Metzler-type state transition matrices we prove the existence of a structurally unique realization having maximal number of non-zero off-diagonal entries. Using an eigenvalue assignment procedure we propose linear programming based algorithms capable of computing different state space realizations. By using the convexity of the feasible set of Metzler-type state transition matrices and results from the theory of non-negative polynomial systems, we provide algorithms to determine structurally different realization. Computational examples are provided to illustrate structural non-uniqueness of network-based LDSs

Advanced and Innovative Optimization Techniques in Controllers: A Comprehensive Review

New commercial power electronic controllers come to the market almost every day to help improve electronic circuit and system performance and efficiency. In DC–DC switching-mode converters, a simple and elegant hysteretic controller is used to regulate the basic buck, boost and buck–boost converters under slightly different configurations. In AC–DC converters, the input current shaping for power factor correction posts a constraint. But, several brilliant commercial controllers are demonstrated for boost and fly back converters to achieve almost perfect power factor correction. In this paper a comprehensive review of the various advanced optimization techniques used in power electronic controllers is presented