Thesis
Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques
- Abstract:
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Gaining insights into complex high-dimensional data is challenging and typically requires the use of dimensionality reduction methods. These methods let us identify low-dimensional structures embedded within the data that may reveal patterns of interest. In probabilistic models, such low-dimensional structures are captured via latent variables.
In biomedical applications, e.g. in computational biology, it is common to use *linear* dimensionality reduction approaches like ...
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Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Programme:
EPSRC & MRC Centre for Doctoral Training (CDT) in Next Generational Statistical Science
Funding agency for:
Martens, K
Funder identifier:
http://dx.doi.org/10.13039/501100000265
+ Medical Research Council
More from this funder
Programme:
EPSRC & MRC Centre for Doctoral Training (CDT) in Next Generational Statistical Science
Funding agency for:
Martens, K
Bibliographic Details
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
Item Description
- Language:
- English
- Keywords:
- Subjects:
- Deposit date:
- 2020-12-29
Related Items
Terms of use
- Copyright holder:
- Märtens, K
- Copyright date:
- 2019
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