What does it mean/signify when the first component covers for more than 99% of the total variance in PCA analysis ? I have a feature vector of size 500X1000 on which I used Matlab's pca function which returns [coeff,score,latent,tsquared,explained]. The variable 'explained' returns the percentage of variance covered by each component.
Significance of 99% of variance covered by the first component in PCA
4.5k views Asked by noob333 At
1
There are 1 answers
Related Questions in MATLAB
- Add additional fields to Linq group by
- couldn't copy pdb file to another directory while consuming wcf web service
- Why are the aliases for string and object in lowercase?
- WPF MessageBox Cancel checkbox check
- Resolve object using DI container with object instance
- Creating a parametrized field name for a SELECT clause
- Does compiler optimize operation on const variable and literal const number?
- Get data from one form to another form in C#
- Writing/Overwriting to specific XML file from ASP.NET code behind
- Deleting Orphans with Fluent NHibernate
Related Questions in PCA
- Add additional fields to Linq group by
- couldn't copy pdb file to another directory while consuming wcf web service
- Why are the aliases for string and object in lowercase?
- WPF MessageBox Cancel checkbox check
- Resolve object using DI container with object instance
- Creating a parametrized field name for a SELECT clause
- Does compiler optimize operation on const variable and literal const number?
- Get data from one form to another form in C#
- Writing/Overwriting to specific XML file from ASP.NET code behind
- Deleting Orphans with Fluent NHibernate
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
Popular Tags
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
The
explainedtells you how accurately you could represent the data by just using that principal component. In your case it means that just using the main principal component, you can describe very accurately (to a 99%) the data.Lets make a 2D example. Imagine you have data that is
100x2and you do PCA.the result could be something like this (taken from the internets)
This data will give you an
explainedvalue for the first principal component (PCA 1st dimension big green arrow in the figure) of around 90%.What does it means?
It means that if you project all your data to that line, you will reconstruct the points with 90% of accuracy (of course, you will loose the information in the PCA 2nd dimension direction).
In your example, with 99% it visually means that almost all the points in blue are laying on the big green arrow, with very little variation in the small green arrow direction.
Of course it is way more difficult to visualize with 1000 dimensions instead of 2, but I hope you understand.