Supervised evaluation of representations
Abstract/Contents
- Abstract
- Intuitively speaking, a good data representation (that is, a dimensionality-reducing mapping) reveals meaningful differences between inputs, while being relatively invariant to differences between inputs due to irrelevant factors or noise. In this work, we consider criteria for formally defining the quality of a representation, which all make use of the availability of a response variable Y to distinguish between meaningful and meaningless variation. Hence, these are criteria for supervised evaluation of representations. We consider three particular criteria: the mutual information between the representation and the response, the average accuracy of a randomized classification task, and the identification accuracy. We discuss methods for estimating all three quantities, and also show how these three quantities are interrelated. Besides the application of evaluating representations, our work also has relevance for estimation of mutual information in high-dimensional data, for obtaining performance guarantees for recognition systems, and for making statements about the generalizability of certain kinds of classification-based experiments which are found in neuroscience.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Zheng, Charles |
|---|---|
| Associated with | Stanford University, Department of Statistics. |
| Primary advisor | Hastie, Trevor |
| Primary advisor | Taylor, Jonathan |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | Taylor, Jonathan |
| Thesis advisor | Efron, Bradley |
| Thesis advisor | Poldrack, Russell A |
| Advisor | Efron, Bradley |
| Advisor | Poldrack, Russell A |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Charles Zheng. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Charles Yang Zheng
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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