By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    football analytics
    The Role of Data Analytics in Football Performance
    9 Min Read
    data Analytics instagram stories
    Data Analytics Helps Marketers Make the Most of Instagram Stories
    15 Min Read
    analyst,women,looking,at,kpi,data,on,computer,screen
    What to Know Before Recruiting an Analyst to Handle Company Data
    6 Min Read
    AI analytics
    AI-Based Analytics Are Changing the Future of Credit Cards
    6 Min Read
    data overload showing data analytics
    How Does Next-Gen SIEM Prevent Data Overload For Security Analysts?
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Weirdness is the “Curse of Dimensionality”
Share
Notification Show More
Aa
SmartData CollectiveSmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > Weirdness is the “Curse of Dimensionality”
Predictive Analytics

Weirdness is the “Curse of Dimensionality”

Editor SDC
Last updated: 2009/03/01 at 11:03 PM
Editor SDC
3 Min Read
SHARE

I read the following well-written section in “The Elements of Statistical Learning” by Friedman, Hastie, & Tibshirani. This curse of dimensionality is profound. I am assuming you are familiar with the k-nearest neighbors classifier, which is used to introduce the idea.

This sparked ideas in two contexts: 1) human personalities and 2) trading.
1) If you think about human personalities being a combination of real-valued variables (ex. introversion-extroversion, affectionate-cold, optimistic-depressed, driven-apathetic, etc) then this basically says that everyone is weird. Let’s say there were only 10 personality traits, then (following the unit 10D-cube example) 90% of people are located over 80% away from the center toward the fringe.
One caveat- this assumes personality traits are uniformly distributed, but due to peer pressure this is probably not the case.
2) You can’t look into the past for a setup identical to what you are currently seeing. Also, the more data streams you feed into a system, and depending on the learner you are using (ex. k-NN), the more every time slice will look absolutely unique and the harder it will be to get a historical data set large enough to teach an…


I read the following well-written section in “The Elements of Statistical Learning” by Friedman, Hastie, & Tibshirani. This curse of dimensionality is profound. I am assuming you are familiar with the k-nearest neighbors classifier, which is used to introduce the idea.

This sparked ideas in two contexts: 1) human personalities and 2) trading.
1) If you think about human personalities being a combination of real-valued variables (ex. introversion-extroversion, affectionate-cold, optimistic-depressed, driven-apathetic, etc) then this basically says that everyone is weird. Let’s say there were only 10 personality traits, then (following the unit 10D-cube example) 90% of people are located over 80% away from the center toward the fringe.
One caveat- this assumes personality traits are uniformly distributed, but due to peer pressure this is probably not the case.
2) You can’t look into the past for a setup identical to what you are currently seeing. Also, the more data streams you feed into a system, and depending on the learner you are using (ex. k-NN), the more every time slice will look absolutely unique and the harder it will be to get a historical data set large enough to teach any trend.

More Read

predictive analytics for amazon pricing

Using Predictive Analytics to Get the Best Deals on Amazon

Predictive Analytics Helps New Dropshipping Businesses Thrive
Promising Benefits of Predictive Analytics in Asset Management
Albanian Bitcoin Investors Tap the Power of Predictive Analytics
Predictive Analytics Improves Trading Decisions as Euro Rebounds

Feel free to add your thoughts, this seems to be a very important result so I’m sure there are more conclusions that can be drawn.

Editor SDC March 1, 2009
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Shutterstock Licensed Photo - 1051059293 | Rawpixel.com
QR Codes Leverage the Benefits of Big Data in Education
Big Data
football analytics
The Role of Data Analytics in Football Performance
Analytics Big Data Exclusive
smart home data
7 Mind-Blowing Ways Smart Homes Use Data to Save Your Money
Big Data
ai low code frameworks
AI Can Help Accelerate Development with Low-Code Frameworks
Artificial Intelligence

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

predictive analytics for amazon pricing
Predictive Analytics

Using Predictive Analytics to Get the Best Deals on Amazon

8 Min Read
predictive analytics in dropshipping
Predictive Analytics

Predictive Analytics Helps New Dropshipping Businesses Thrive

12 Min Read
analyst,women,looking,at,kpi,data,on,computer,screen
Predictive Analytics

Promising Benefits of Predictive Analytics in Asset Management

11 Min Read
predictive analytics helps Albanian bitcoin investors
Blockchain

Albanian Bitcoin Investors Tap the Power of Predictive Analytics

9 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?