title: Maximum margin principal components

authors: Luo, Xianghui

subjects: Machine learning

abstract: Principal Component Analysis (PCA) is a very successful dimensionality reduction technique, widely used in predictive modeling. A key factor in its widespread use in this domain is the fact that the projection of a dataset onto its first $K$ principal components minimizes the sum of squared errors between the original data and the projected data over all possible rank $K$ projections. Thus, PCA provides optimal low-rank representations of data for least-squares linear regression under standard modeling assumptions. On the other hand, when the loss function for a prediction problem is not the least-squares error, PCA is typically a heuristic choice of dimensionality reduction -- in particular for classification problems under the zero-one loss. In this paper we target classification problems by proposing a straightforward alternative to PCA that aims to minimize the difference in margin distribution between the original and the projected data. Extensive experiments show that our simple approach typically outperforms PCA on any particular dataset, in terms of classification error, though this difference is not always statistically significant, and despite being a filter method is frequently competitive with Partial Least Squares (PLS) and Lasso on a wide range of datasets.

text: Maximum margin principal components Principal Component Analysis (PCA) is a very successful dimensionality reduction technique, widely used in predictive modeling. A key factor in its widespread use in this domain is the fact that the projection of a dataset onto its first $K$ principal components minimizes the sum of squared errors between the original data and the projected data over all possible rank $K$ projections. Thus, PCA provides optimal low-rank representations of data for least-squares linear regression under standard modeling assumptions. On the other hand, when the loss function for a prediction problem is not the least-squares error, PCA is typically a heuristic choice of dimensionality reduction -- in particular for classification problems under the zero-one loss. In this paper we target classification problems by proposing a straightforward alternative to PCA that aims to minimize the difference in margin distribution between the original and the projected data. Extensive experiments show that our simple approach typically outperforms PCA on any particular dataset, in terms of classification error, though this difference is not always statistically significant, and despite being a filter method is frequently competitive with Partial Least Squares (PLS) and Lasso on a wide range of datasets.

year: 2017

title: Refinement for user interface designs

authors: Bowen, Judy

subjects: refinement

abstract: Formal approaches to software development require that we correctly describe (or specify) systems in order to prove properties about our proposed solution prior to building it. We must then follow a rigorous process to transform our specification into an implementation to ensure that the properties we have proved are retained. Different transformation, or refinement, methods exist for different formal methods, but they all seek to ensure that we can guide the transformation in a way which preserves the desired properties of the system. Refinement methods also allow us to subsequently compare two systems to see if a refinement relation exists between the two. When we design and build the user interfaces of our systems we are similarly keen to ensure that they have certain properties before we build them. For example, do they satisfy the requirements of the user? Are they designed with known good design principles and usability considerations in mind? Are they correct in terms of the overall system specification? However, when we come to implement our interface designs we do not have a defined process to follow which ensures that we maintain these properties as we transform the design into code. Instead, we rely on our judgement and belief that we are doing the right thing and subsequent user testing to ensure that our final solution remains useable and satisfactory. We suggest an alternative approach, which is to define a refinement process for user interfaces which will allow us to maintain the same rigorous standards we apply to the rest of the system when we implement our user interface designs.

text: Refinement for user interface designs Formal approaches to software development require that we correctly describe (or specify) systems in order to prove properties about our proposed solution prior to building it. We must then follow a rigorous process to transform our specification into an implementation to ensure that the properties we have proved are retained. Different transformation, or refinement, methods exist for different formal methods, but they all seek to ensure that we can guide the transformation in a way which preserves the desired properties of the system. Refinement methods also allow us to subsequently compare two systems to see if a refinement relation exists between the two. When we design and build the user interfaces of our systems we are similarly keen to ensure that they have certain properties before we build them. For example, do they satisfy the requirements of the user? Are they designed with known good design principles and usability considerations in mind? Are they correct in terms of the overall system specification? However, when we come to implement our interface designs we do not have a defined process to follow which ensures that we maintain these properties as we transform the design into code. Instead, we rely on our judgement and belief that we are doing the right thing and subsequent user testing to ensure that our final solution remains useable and satisfactory. We suggest an alternative approach, which is to define a refinement process for user interfaces which will allow us to maintain the same rigorous standards we apply to the rest of the system when we implement our user interface designs.

year: 2009

title: Street health: Practitioner service provision for Maori homeless people in Auckland

authors: Nikora, Linda Waimarie

abstract: Drawing insights from interviews with Maori homeless people, health professionals, and relevant local and international literatures, this chapter focuses on the provision of medical care to homeless people. In particular, we propose that health services orientate to accommodate the worldviews and circumstances of Maori homeless people. Below we consider colonialism and societal developments that have led to homelessness among Maori today. We then present a case study of ‘Grant’, which was compiled from common aspects of various Maori homeless people who access health services at the Auckland City Mission (ACM); an organisation with a long history of catering to the needs and hopes of dispossessed groups, providing food, clothing, advocacy, social and health services. The relational orientation of healthcare at the ACM is discussed, and leads to an exploration of ‘judgement-free service space’ for meeting client needs (cf., Trussell & Mair, 2010). Lastly, we focus on how health professionals can respond to the multiple healthcare needs of Maori homeless people, in partnership with social services.

text: Street health: Practitioner service provision for Maori homeless people in Auckland Drawing insights from interviews with Maori homeless people, health professionals, and relevant local and international literatures, this chapter focuses on the provision of medical care to homeless people. In particular, we propose that health services orientate to accommodate the worldviews and circumstances of Maori homeless people. Below we consider colonialism and societal developments that have led to homelessness among Maori today. We then present a case study of ‘Grant’, which was compiled from common aspects of various Maori homeless people who access health services at the Auckland City Mission (ACM); an organisation with a long history of catering to the needs and hopes of dispossessed groups, providing food, clothing, advocacy, social and health services. The relational orientation of healthcare at the ACM is discussed, and leads to an exploration of ‘judgement-free service space’ for meeting client needs (cf., Trussell & Mair, 2010). Lastly, we focus on how health professionals can respond to the multiple healthcare needs of Maori homeless people, in partnership with social services.

year: 2012-09