Semantic Models for Machine Learning

In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications. The applied approaches obtain new feature directions on data, such that machine learning applications would show an increase in performance. We review three principle methods that are used throughout the thesis. Firstly Canonical Correlation Analysis (CCA), which is a method of correlating linear relationships between two multidimensional variables. CCA can be seen as using complex labels as a way of guiding feature selection towards the underlying semantics. CCA makes use of two views of the same semantic object to extract a representation of the semantics. Secondly Partial Least Squares (PLS), a method similar to CCA. It selects feature directions that are useful for the task at hand, though PLS only uses one view of an object and the label as the corresponding pair. PLS could be thought of as a method that looks for directions that are good for distinguishing the different labels. The third method is the Fisher kernel. A method that aims to extract more information of a generative model than simply by their output probabilities. The aim is to analyse how the Fisher score depends on the model and which aspects of the model are important in determining the Fisher score. We focus our theoretical investigation primarily on CCA and its kernel variant. Providing a theoretical analysis of the method's stability using Rademacher complexity, hence deriving the error bound for new data. We conclude the thesis by applying the described approaches to problems in the various fields of image, text, music application and medical analysis, describing several novel applications on relevant real-world data. The aim of the thesis is to provide a theoretical understanding of semantic models, while also providing a good application foundation on how these models can be practically used

Semantic models for machine learning

In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications. The applied approaches obtain new feature directions on data, such that machine learning applications would show an increase in performance. We review three principle methods that are used throughout the thesis. Firstly Canonical Correlation Analysis (CCA), which is a method of correlating linear relationships between two multidimensional variables. CCA can be seen as using complex labels as a way of guiding feature selection towards the underlying semantics. CCA makes use of two views of the same semantic object to extract a representation of the semantics. Secondly Partial Least Squares (PLS), a method similar to CCA. It selects feature directions that are useful for the task at hand, though PLS only uses one view of an object and the label as the corresponding pair. PLS could be thought of as a method that looks for directions that are good for distinguishing the different labels. The third method is the Fisher kernel. A method that aims to extract more information of a generative model than simply by their output probabilities. The aim is to analyse how the Fisher score depends on the model and which aspects of the model are important in determining the Fisher score. We focus our theoretical investigation primarily on CCA and its kernel variant. Providing a theoretical analysis of the method's stability using Rademacher complexity, hence deriving the error bound for new data. We conclude the thesis by applying the described approaches to problems in the various fields of image, text, music application and medical analysis, describing several novel applications on relevant real-world data. The aim of the thesis is to provide a theoretical understanding of semantic models, while also providing a good application foundation on how these models can be practically used.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Ranking algorithms for implicit feedback

This report presents novel algorithms to use eye movements as an implicit relevance feedback in order to improve the performance of the searches. The algorithms are evaluated on "Transport Rank Five" Dataset which were previously collected in Task 8.3. We demonstrated that simple linear combination or tensor product of eye movement and image features can improve the retrieval accuracy

Prescribing of antipsychotics among people with recorded personality disorder in primary care: a retrospective nationwide cohort study using The Health Improvement Network primary care database.

Objectives: To investigate the extent of antipsychotic prescribing to people with recorded personality disorder (PD) in UK primary care and factors associated with such prescribing. // Design: Retrospective cohort study. // Setting: General practices contributing to The Health Improvement Network UK-wide primary care database, 1 January 2000–31 December 2016. // Participants: 46 210 people registered with participating general practices who had a record of PD in their general practice notes. 1358 (2.9%) people with missing deprivation information were excluded from regression analyses; no other missing data. // Main outcome measures: Prescriptions for antipsychotics in general practice records and length of time in receipt of antipsychotic prescriptions. // Results: Of 46 210 people with recorded PD, 15 562 (34%) were ever prescribed antipsychotics. Among the subgroup of 36 875 people with recorded PD, but no recorded severe mental illness (SMI), 9208 (25%) were prescribed antipsychotics; prescribing was lower in less deprived areas (adjusted rate ratio (aRR) comparing least to most deprived quintile: 0.56, 95% CI 0.48 to 0.66, p<0.001), was higher in females (aRR:1.25, 95% CI 1.16 to 1.34, p<0.001) and with a history of adverse childhood experiences (aRR:1.44, 95% CI 1.28 to 1.56, p<0.001). Median time prescribed antipsychotics was 605 days (IQR 197–1639 days). Prescribing frequency has increased over time. // Conclusions: Contrary to current UK guidelines, antipsychotics are frequently and increasingly prescribed for extended periods to people with recorded PD, but with no history of SMI. An urgent review of clinical practice is warranted, including the effectiveness of such prescribing and the need to monitor for adverse effects, including metabolic complications

Association between quetiapine use and self-harm outcomes among people with recorded personality disorder in UK primary care: A self-controlled case series analysis

BACKGROUND: Quetiapine is frequently prescribed to people with personality disorder diagnoses, but this is not supported by evidence or treatment guidelines. AIMS: To examine associations between periods of quetiapine prescribing and self-harm events in people with personality disorder. METHOD: Self-controlled case series using linked primary care and hospital records covering the period 2007-2017. We calculated incidence rates and incidence rate ratios (IRRs) for self-harm events during periods when people were prescribed (exposed to) quetiapine, as well as periods when they were unexposed or pre-exposed to quetiapine. RESULTS: We analysed data from 1,082 individuals with established personality disorder diagnoses, all of whom had at least one period of quetiapine prescribing and at least one self-harm episode. Their baseline rate of self-harm (greater than 12 months before quetiapine treatment) was 0.52 episodes per year. Self-harm rates were elevated compared to the baseline rate in the month after quetiapine treatment was commenced (IRR 1.85; 95% confidence interval (CI) 1.46-2.34) and remained raised throughout the year after quetiapine treatment was started. However, self-harm rates were highest in the month prior to quetiapine initiation (IRR 3.59; 95% CI 2.83-4.55) and were elevated from 4 months before quetiapine initiation, compared to baseline. CONCLUSION: Self-harm rates were elevated throughout the first year of quetiapine prescribing, compared to the baseline rate. However, rates of self-harm reduced in the month after patients commenced quetiapine, compared to the month before quetiapine was initiated. Self-harm rates gradually dropped over a year of quetiapine treatment. Quetiapine may acutely reduce self-harm. Longer-term use and any potential benefits need to be balanced with the risk of adverse events

Overcoming Status Quo Bias: Nudging in a Government-Led Digital Transformation Initiative

While Singaporean citizens are keen on using e-payments in retail shops, they still prefer cash payments in hawker centers and coffee shops, i.e., traditional open-air complexes selling inexpensive cooked food. A government-led initiative seeks to tackle the situation, known as status quo bias. The key actors involved in this initiative are public agencies, the central bank, and a private Singaporean electronic payment service provider. Working with these partners, we investigate the process designed to nudge citizens to use e-payments for micropayments in hawker centers and coffee shops. We employ a design ethnography methodology and adapt an existing nudging framework. Early findings reveal contingency factors that shape the nudging approach. Through this study, we expect to contribute to the theoretical development of nudging theory to overcome status quo bias in government-led digital transformation initiatives