title: Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions

authors: Durrant, Robert J.

subjects: random projections

abstract: We prove theoretical guarantees for an averaging-ensemble of randomly projected Fisher linear discriminant classifiers, focusing on the casewhen there are fewer training observations than data dimensions. The specific form and simplicity of this ensemble permits a direct and much more detailed analysis than existing generic tools in previous works. In particular, we are able to derive the exact form of the generalization error of our ensemble, conditional on the training set, and based on this we give theoretical guarantees which directly link the performance of the ensemble to that of the corresponding linear discriminant learned in the full data space. To the best of our knowledge these are the first theoretical results to prove such an explicit link for any classifier and classifier ensemble pair. Furthermore we show that the randomly projected ensemble is equivalent to implementing a sophisticated regularization scheme to the linear discriminant learned in the original data space and this prevents overfitting in conditions of small sample size where pseudo-inverse FLD learned in the data space is provably poor. Our ensemble is learned from a set of randomly projected representations of the original high dimensional data and therefore for this approach data can be collected, stored and processed in such a compressed form. We confirm our theoretical findings with experiments, and demonstrate the utility of our approach on several datasets from the bioinformatics domain and one very high dimensional dataset from the drug discovery domain, both settings in which fewer observations than dimensions are the norm.

text: Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions We prove theoretical guarantees for an averaging-ensemble of randomly projected Fisher linear discriminant classifiers, focusing on the casewhen there are fewer training observations than data dimensions. The specific form and simplicity of this ensemble permits a direct and much more detailed analysis than existing generic tools in previous works. In particular, we are able to derive the exact form of the generalization error of our ensemble, conditional on the training set, and based on this we give theoretical guarantees which directly link the performance of the ensemble to that of the corresponding linear discriminant learned in the full data space. To the best of our knowledge these are the first theoretical results to prove such an explicit link for any classifier and classifier ensemble pair. Furthermore we show that the randomly projected ensemble is equivalent to implementing a sophisticated regularization scheme to the linear discriminant learned in the original data space and this prevents overfitting in conditions of small sample size where pseudo-inverse FLD learned in the data space is provably poor. Our ensemble is learned from a set of randomly projected representations of the original high dimensional data and therefore for this approach data can be collected, stored and processed in such a compressed form. We confirm our theoretical findings with experiments, and demonstrate the utility of our approach on several datasets from the bioinformatics domain and one very high dimensional dataset from the drug discovery domain, both settings in which fewer observations than dimensions are the norm.

year: 2014-08-19

title: Ūloa: Rethinking mental health provision in Pacific Island communities

authors: Vaka, Sione

abstract: Modern mental health services fail to effectively cater for migrant Pacific Island communities, which have different worldviews and ways of knowing. At the University of Waikato, Associate Professor Sione Vaka has developed the ūloa model of care, which draws inspiration from traditional research methods and ways of life. The ūloa model offers Pacific-centred mental health services with cultural appropriateness and equitable outcomes.

text: Ūloa: Rethinking mental health provision in Pacific Island communities Modern mental health services fail to effectively cater for migrant Pacific Island communities, which have different worldviews and ways of knowing. At the University of Waikato, Associate Professor Sione Vaka has developed the ūloa model of care, which draws inspiration from traditional research methods and ways of life. The ūloa model offers Pacific-centred mental health services with cultural appropriateness and equitable outcomes.

year: 2025

title: “If it don’t talk, it's not whakapapa data”: Conceptualising whakapapa data in a digital age

authors: Pēpi Tarapa‐Dewes, Ella

subjects: data protection

abstract: In Te Ao Māori, whakapapa is a sacred framework that connects all aspects of existence. Whakapapa is highly sensitive information and a taonga that requires active protection. But what can be considered whakapapa data in a digital age where data linkage is ubiquitous and the boundaries between personal and collective information are increasingly blurred? We explore this question through a case study with Ngāti Tiipa, a hapū that is actively engaged in its own data sovereignty research. Reflexive thematic analysis of whānau kōrero identified three types of whakapapa data, each denoting a specific kind of connection: 1) between relatives, both living and deceased; 2) between people and places of cultural significance; and 3) between people and the spiritual realm. Whānau concepts of whakapapa data tend to be context‐specific, suggesting that it is more useful to think of whakapapa data as dynamic and relational rather than a fixed, inherent attribute.

text: “If it don’t talk, it's not whakapapa data”: Conceptualising whakapapa data in a digital age In Te Ao Māori, whakapapa is a sacred framework that connects all aspects of existence. Whakapapa is highly sensitive information and a taonga that requires active protection. But what can be considered whakapapa data in a digital age where data linkage is ubiquitous and the boundaries between personal and collective information are increasingly blurred? We explore this question through a case study with Ngāti Tiipa, a hapū that is actively engaged in its own data sovereignty research. Reflexive thematic analysis of whānau kōrero identified three types of whakapapa data, each denoting a specific kind of connection: 1) between relatives, both living and deceased; 2) between people and places of cultural significance; and 3) between people and the spiritual realm. Whānau concepts of whakapapa data tend to be context‐specific, suggesting that it is more useful to think of whakapapa data as dynamic and relational rather than a fixed, inherent attribute.

year: 2026-02