Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques

Martens, K

Abstract:

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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APA Style

Martens, K. (2019). Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques [PhD thesis]. University of Oxford.

MLA Style

Martens, K. Enabling Feature-Level Interpretability in Non-Linear Latent Variable Models: a Synthesis of Statistical and Machine Learning Techniques. University of Oxford, 2019.

Chicago Style

Martens, K. 2019. “Enabling Feature-Level Interpretability in Non-Linear Latent Variable Models: a Synthesis of Statistical and Machine Learning Techniques.” PhD thesis, University of Oxford.
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