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

authors: Durrant, Robert J.

subjects: ensemble classifiers

abstract: We examine the performance of an ensemble of randomly-projected Fisher Linear Discriminant classifiers, focusing on the case when there are fewer training observations than data dimensions. Our ensemble is learned from a sequence 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. 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.

text: Random projections as regularizers: learning a linear discriminant ensemble from fewer observations than dimensions We examine the performance of an ensemble of randomly-projected Fisher Linear Discriminant classifiers, focusing on the case when there are fewer training observations than data dimensions. Our ensemble is learned from a sequence 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. 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.

year: 2013-11-13

title: Time-evolving data science and artificial intelligence for Advanced Open Environmental Science (TAIAO) programme

authors: Koh, Yun Sing

subjects: computer science

abstract: New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues,  advanced data science and artificial intelligence techniques can provide unique solutions. Currently, in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans.

text: Time-evolving data science and artificial intelligence for Advanced Open Environmental Science (TAIAO) programme New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues,  advanced data science and artificial intelligence techniques can provide unique solutions. Currently, in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans.

year: 2024

title: The Control, environmental sustainability and information management of Maori land in New Zealand

authors: Morad, Munir

subjects: Maori land

abstract: An increase in Maori land claims and the subsequent settlement of a number of land confiscation grievances have prompted calls for change to the land management, cadastral and legal regimes in New Zealand. There is little expert agreement as to Me shape any such reforms should take, but environmental planners and Maori leaders have conceded the need for an overhaul of current land information management practices. Such reforms must aspire to being legally workable, culturally appropriate and environmentally sustainable.
Most land in New Zealand is subject to recent legislation (post-1984) which acknowledges concern for Maori cultural values and ancestral rights and environmental sustainability. The existence of this legislation and growing Maori involvement in formulating public policy for the management of land and environmental resources mean that Maori concepts of land management and sustainability are receiving increasing attention and are likely to influence the shape of future cadastral reforms for Maori lands.

text: The Control, environmental sustainability and information management of Maori land in New Zealand An increase in Maori land claims and the subsequent settlement of a number of land confiscation grievances have prompted calls for change to the land management, cadastral and legal regimes in New Zealand. There is little expert agreement as to Me shape any such reforms should take, but environmental planners and Maori leaders have conceded the need for an overhaul of current land information management practices. Such reforms must aspire to being legally workable, culturally appropriate and environmentally sustainable.
Most land in New Zealand is subject to recent legislation (post-1984) which acknowledges concern for Maori cultural values and ancestral rights and environmental sustainability. The existence of this legislation and growing Maori involvement in formulating public policy for the management of land and environmental resources mean that Maori concepts of land management and sustainability are receiving increasing attention and are likely to influence the shape of future cadastral reforms for Maori lands.

year: 1997