595 research outputs found
Covariate-invariant gait analysis for human identification(人識別を目的とする共変量不変歩行解析)
信州大学(Shinshu university)博士(工学)ThesisYEOH TZE WEI. Covariate-invariant gait analysis for human identification(人識別を目的とする共変量不変歩行解析). 信州大学, 2018, 博士論文. 博士(工学), 甲第692号, 平成30年03月20日授与.doctoral thesi
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A novel deep mining model for effective knowledge discovery from omics data
Knowledge discovery from omics data has become a common goal of current approaches to personalised cancer medicine and understanding cancer genotype and phenotype. However, high-throughput biomedical datasets are characterised by high dimensionality and relatively small sample sizes with small signal-to-noise ratios. Extracting and interpreting relevant knowledge from such complex datasets therefore remains a significant challenge for the fields of machine learning and data mining. In this paper, we exploit recent advances in deep learning to mitigate against these limitations on the basis of automatically capturing enough of the meaningful abstractions latent with the available biological samples. Our deep feature learning model is proposed based on a set of non-linear sparse Auto-Encoders that are deliberately constructed in an under-complete manner to detect a small proportion of molecules that can recover a large proportion of variations underlying the data. However, since multiple projections are applied to the input signals, it is hard to interpret which phenotypes were responsible for deriving such predictions. Therefore, we also introduce a novel weight interpretation technique that helps to deconstruct the internal state of such deep learning models to reveal key determinants underlying its latent representations. The outcomes of our experiment provide strong evidence that the proposed deep mining model is able to discover robust biomarkers that are positively and negatively associated with cancers of interest. Since our deep mining model is problem-independent and data-driven, it provides further potential for this research to extend beyond its cognate disciplines
FUNCK: Information Funnels and Bottlenecks for Invariant Representation Learning
Learning invariant representations that remain useful for a downstream task
is still a key challenge in machine learning. We investigate a set of related
information funnels and bottleneck problems that claim to learn invariant
representations from the data. We also propose a new element to this family of
information-theoretic objectives: The Conditional Privacy Funnel with Side
Information, which we investigate in fully and semi-supervised settings. Given
the generally intractable objectives, we derive tractable approximations using
amortized variational inference parameterized by neural networks and study the
intrinsic trade-offs of these objectives. We describe empirically the proposed
approach and show that with a few labels it is possible to learn fair
classifiers and generate useful representations approximately invariant to
unwanted sources of variation. Furthermore, we provide insights about the
applicability of these methods in real-world scenarios with ordinary tabular
datasets when the data is scarce.Comment: 28 page
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