EDA-DR-Concepts

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.

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Objectives: ...

Outcomes: ...

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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.

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Prerequisites:

• EDA-Concepts (Concepts of Exploratory Data Analysis (EDA))

 

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Evaluation

Evaluation: NA

This unit is not evaluated for course marks.

Contents

Task:

• Read the introductory notes on dimension reduction and related methods for exploratory data analysis.

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

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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.