MATLAB and Simulink resources for Arduino, LEGO, and Raspberry Pi
Learn moreOpportunities for recent engineering grads.
Apply TodayNew to MATLAB?
MATLAB Central > MATLAB Newsreader > PCA dimensionality reduction / component identific... 

Subject: PCA dimensionality reduction / component identifications From: Pierrre Gogin Date: 16 Jul, 2010 16:02:06 Message: 1 of 18 
Hi everybody, 
Subject: PCA dimensionality reduction / component identifications From: Peter Perkins Date: 16 Jul, 2010 18:10:19 Message: 2 of 18 
On 7/16/2010 12:02 PM, Pierrre Gogin wrote: 
Subject: PCA dimensionality reduction / component identifications From: Pierrre Gogin Date: 16 Jul, 2010 20:13:21 Message: 3 of 18 
Hi Peter, 
Subject: PCA dimensionality reduction / component identifications From: Peter Perkins Date: 16 Jul, 2010 21:10:15 Message: 4 of 18 
On 7/16/2010 4:13 PM, Pierrre Gogin wrote: 
Subject: PCA dimensionality reduction / component identifications From: Pierrre Gogin Date: 17 Jul, 2010 15:15:21 Message: 5 of 18 
Hi Peter, 
Subject: PCA dimensionality reduction / component identifications From: Peter Perkins Date: 17 Jul, 2010 16:38:24 Message: 6 of 18 
On 7/17/2010 11:15 AM, Pierrre Gogin wrote: 
Subject: PCA dimensionality reduction / component identifications From: Pierrre Gogin Date: 17 Jul, 2010 22:10:06 Message: 7 of 18 
Hi Peter, 
Subject: PCA dimensionality reduction / component identifications From: Peter Perkins Date: 19 Jul, 2010 14:57:40 Message: 8 of 18 
On 7/17/2010 6:10 PM, Pierrre Gogin wrote: 
Subject: PCA dimensionality reduction / component identifications From: Rob Campbell Date: 19 Jul, 2010 18:05:10 Message: 9 of 18 
>know, with which percentage each of my three features (vec1,vec2,vec3) from the 
Subject: PCA dimensionality reduction / component identifications From: Philip Mewes Date: 20 Jul, 2010 14:39:04 Message: 10 of 18 

Subject: PCA dimensionality reduction / component identifications From: Rob Campbell Date: 20 Jul, 2010 15:30:22 Message: 11 of 18 

Subject: PCA dimensionality reduction / component identifications From: Rob Campbell Date: 20 Jul, 2010 15:35:20 Message: 12 of 18 
>The reduction itself is not the problem, but I could not figure out, how to indentify the 
Subject: PCA dimensionality reduction / component identifications From: Philip Mewes Date: 20 Jul, 2010 15:36:04 Message: 13 of 18 

Subject: PCA dimensionality reduction / component identifications From: Philip Mewes Date: 20 Jul, 2010 15:47:05 Message: 14 of 18 
Hi Rob, 
Subject: PCA dimensionality reduction / component identifications From: Rob Campbell Date: 20 Jul, 2010 16:11:23 Message: 15 of 18 
Generally people will: 
Subject: PCA dimensionality reduction / component identifications From: Philip Mewes Date: 20 Jul, 2010 16:12:05 Message: 16 of 18 
"Rob Campbell" <matlab@robertREMOVEcampbell.removethis.co.uk> wrote in message <i24fno$6s3$1@fred.mathworks.com>... 
Subject: PCA dimensionality reduction / component identifications From: Rob Campbell Date: 20 Jul, 2010 16:18:04 Message: 17 of 18 
Ah! So you have two groups and you can produce a training set where you know whether or not each image contains a car? In that case, you have a supervised classification problem. PCA isn't the right way to go. Why not conduct a discriminant analysis? This will produce a single direction in your space which best separates the car from noncar images. You can plot your data as two histograms along this axis. The direction of the vector will tell you the basis upon which the discrimination was made: each of your original variables will have a "weighting" and you can use the magnitude of each weighting to decide whether or not it is significant. You can calculate confidence intervals for these weightings (maybe using a permutation test) to help you determine significance. 
Subject: PCA dimensionality reduction / component identifications From: Greg Heath Date: 21 Jul, 2010 04:58:48 Message: 18 of 18 
On Jul 16, 12:02 pm, "Pierrre Gogin" <pierre.go...@freemail.de> wrote: 
A tag is like a keyword or category label associated with each thread. Tags make it easier for you to find threads of interest.
Anyone can tag a thread. Tags are public and visible to everyone.