Difference between revisions of "EDA-DR-Concepts"
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Concepts of Dimension Reduction | Concepts of Dimension Reduction | ||
| + | <div style="padding:5px; margin-top:20px; margin-bottom:10px; background-color:#f2fafa; font-size:30%; font-weight:200; color: #000000; "> | ||
| + | (Concepts of Dimension Reduction) | ||
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| − | + | <b>Abstract:</b><br /> | |
<section begin=abstract /> | <section begin=abstract /> | ||
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This unit discusses the "curse of dimensionality" in data mining, and introduces ideas and strategies how dimension reduction can address this problem in bioinformatics. Also: dimension reduction to create features for machine learning. | This unit discusses the "curse of dimensionality" in data mining, and introduces ideas and strategies how dimension reduction can address this problem in bioinformatics. Also: dimension reduction to create features for machine learning. | ||
<section end=abstract /> | <section end=abstract /> | ||
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| − | + | <table> | |
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| − | + | <b>Objectives:</b><br /> | |
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... | ... | ||
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| − | + | <b>Outcomes:</b><br /> | |
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... | ... | ||
| − | + | </td> | |
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| − | + | </table> | |
| − | + | <!-- ============================ --> | |
| − | === | + | <hr> |
| − | < | + | <b>Deliverables:</b><br /> |
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<!-- included from "./data/ABC-unit_components.txt", section: "deliverables-time_management" --> | <!-- included from "./data/ABC-unit_components.txt", section: "deliverables-time_management" --> | ||
*<b>Time management</b>: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit. | *<b>Time management</b>: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit. | ||
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<!-- included from "./data/ABC-unit_components.txt", section: "deliverables-insights" --> | <!-- included from "./data/ABC-unit_components.txt", section: "deliverables-insights" --> | ||
*<b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|'''insights!''' page]]. | *<b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|'''insights!''' page]]. | ||
| + | <section end=deliverables /> | ||
| + | <!-- ============================ --> | ||
| + | <hr> | ||
| + | <section begin=prerequisites /> | ||
| + | <b>Prerequisites:</b><br /> | ||
| + | *[[EDA-Concepts|EDA-Concepts (Concepts of Exploratory Data Analysis (EDA))]] | ||
| + | <section end=prerequisites /> | ||
| + | <!-- ============================ --> | ||
| + | </div> | ||
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| − | + | __TOC__ | |
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{{Vspace}} | {{Vspace}} | ||
| − | == | + | == Contents == |
| − | <!-- included from "./components/EDA-DR-Concepts.components.txt", section: " | + | <!-- included from "./components/EDA-DR-Concepts.components.txt", section: "contents" --> |
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| + | {{Task|1= | ||
| + | * Read the introductory notes on {{ABC-PDF|EDA-DR-Concepts|dimension reduction and related methods for exploratory data analysis}}. | ||
| + | }} | ||
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== Self-evaluation == | == Self-evaluation == | ||
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=== Question 1=== | === Question 1=== | ||
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| − | + | == Notes == | |
| − | {{ | + | <!-- included from "./components/EDA-DR-Concepts.components.txt", section: "notes" --> |
| − | + | <!-- included from "./data/ABC-unit_components.txt", section: "notes" --> | |
| − | + | <references /> | |
| + | == Further reading, links and resources == | ||
| + | <!-- Formatting exqmples: | ||
| + | {{#pmid: 19957275}} | ||
| + | <div class="reference-box">[http://www.ncbi.nlm.nih.gov]</div> | ||
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Revision as of 19:32, 26 January 2018
Concepts of Dimension Reduction
(Concepts of Dimension Reduction)
Abstract:
This unit discusses the "curse of dimensionality" in data mining, and introduces ideas and strategies how dimension reduction can address this problem in bioinformatics. Also: dimension reduction to create features for machine learning.
|
Objectives: |
Outcomes: |
Deliverables:
- Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
- Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
- Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.
Prerequisites:
Sorry!
This page is only a stub; it is here as a placeholder to establish the logical framework of the site but there is no significant content as yet. Do not work with this material until it is updated to "live" status.
Contents
Task:
- Read the introductory notes on dimension reduction and related methods for exploratory data analysis.
Self-evaluation
Notes
Further reading, links and resources
If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.
About ...
Author:
- Boris Steipe <boris.steipe@utoronto.ca>
Created:
- 2017-09-17
Modified:
- 2017-09-18
Version:
- 0.1
Version history:
- 0.1 First stub
This copyrighted material is licensed under a Creative Commons Attribution 4.0 International License. Follow the link to learn more.
