Difference between revisions of "EDA-DR-Concepts"

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Concepts of Dimension Reduction
 
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<b>Keywords:</b>&nbsp;
 
 
Concepts of Dimension Reduction
 
Concepts of Dimension Reduction
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<div style="padding:5px; margin-top:20px; margin-bottom:10px; background-color:#f2fafa; font-size:30%; font-weight:200; color: #000000; ">
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(Concepts of Dimension Reduction)
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__TOC__
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<div style="font-size:118%;">
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<b>Abstract:</b><br />
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{{STUB}}
 
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== Abstract ==
 
 
<section begin=abstract />
 
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<!-- included from "../components/EDA-DR-Concepts.components.wtxt", section: "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.
 
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.
 
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<!-- ============================  -->
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== This unit ... ==
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=== Prerequisites ===
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<b>Objectives:</b><br />
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<b>Outcomes:</b><br />
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<!-- ============================ -->
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<b>Deliverables:</b><br />
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<li><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.</li>
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<li><b>Journal</b>: Document your progress in your [[FND-Journal|Course Journal]]. Some tasks may ask you to include specific items in your journal. Don't overlook these.</li>
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<li><b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|'''insights!''' page]].</li>
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<section end=deliverables />
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<section begin=prerequisites />
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<b>Prerequisites:</b><br />
 
*[[EDA-Concepts|EDA-Concepts (Concepts of Exploratory Data Analysis (EDA))]]
 
*[[EDA-Concepts|EDA-Concepts (Concepts of Exploratory Data Analysis (EDA))]]
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<section end=prerequisites />
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=== Objectives ===
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=== Outcomes ===
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=== Deliverables ===
 
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*<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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*<b>Journal</b>: Document your progress in your [[FND-Journal|Course Journal]]. Some tasks may ask you to include specific items in your journal. Don't overlook these.
 
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<!-- included from "ABC-unit_components.wtxt", section: "deliverables-insights" -->
 
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*<b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|'''insights!''' page]].
 
  
 
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=== Evaluation ===
 
=== Evaluation ===
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<b>Evaluation: NA</b><br />
 
<b>Evaluation: NA</b><br />
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:This unit is not evaluated for course marks.
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<div style="margin-left: 2rem;">This unit is not evaluated for course marks.</div>
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== Contents ==
 
== Contents ==
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{{Task|1=
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* Read the introductory notes on {{ABC-PDF|EDA-DR-Concepts|dimension reduction and related methods for exploratory data analysis}}.
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== Further reading, links and resources ==
 
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{{#pmid: 19957275}}
 
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<div class="reference-box">[http://www.ncbi.nlm.nih.gov]</div>
 
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== Notes ==
 
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== Self-evaluation ==
 
== Self-evaluation ==
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== Further reading, links and resources ==
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{{#pmid: 19957275}}
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<div class="reference-box">[http://www.ncbi.nlm.nih.gov]</div>
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== Notes ==
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<references />
  
 
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<b>If in doubt, ask!</b> 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.
 
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Latest revision as of 09:27, 25 September 2020

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.


     



     


    Evaluation

    Evaluation: NA

    This unit is not evaluated for course marks.

    Contents

    Task:

    Self-evaluation

    Further reading, links and resources

    Notes


     


    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

    CreativeCommonsBy.png This copyrighted material is licensed under a Creative Commons Attribution 4.0 International License. Follow the link to learn more.

    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.

    Retrieved from "http://steipe.biochemistry.utoronto.ca/abc/index.php?title=EDA-DR-Concepts&oldid=11708"