Return to Question

I didn't know this forum existed so I asked this on Stack Overflow but maybe it's more suitable here...

I have a question regarding dimensionality reduction using PCA.

If I have a train set (train.arff, 10 attributes) I perform a PCA and I save my data with respect to the new transformed variables (say I choose the two first attributes, combination of the original ones, that collect most of the variance), and call this transformed trainset "trainset-afterPCA.arff". Now I train a model using this file (which only has 2 attributes), and save it.

Update: Just to show (part) of the weka output:

If I have a train set (train.arff, 10 attributes) I perform a PCA and I save my data with respect to the new transformed variables (say I choose the two first attributes, combination of the original ones, that collect most of the variance), and call this transformed trainset "trainset-afterPCA.arff". Now I train a model using this file (which only has 2 attributes), and save it.

Update:

Just to show (part) of the Weka output:

Update: Just to show (part) of the weka output:

eigenvalue  proportion  cumulative
2.31715   0.28964     0.28964   0.512ent-0.472Threshold+0.422impRes-0.335pssm-mut+0.28 pssm-wt...
1.72263   0.21533     0.50497   0.593pssm-mut+0.501pssm-wt+0.41 Threshold+0.403hyd+0.161sub...
1.31987   0.16498     0.66996   0.698vdw+0.628sub+0.219Threshold-0.168hyd+0.154ent...
0.88362   0.11045     0.78041   0.53impRes-0.51pssm-wt-0.478ent+0.346hyd+0.33 subs-score...
0.8404    0.10505     0.88546   0.605hyd-0.552impRes-0.319pssm-wt-0.26pssm-mut+0.235ent...
0.56935   0.07117     0.95663   -0.656vdw+0.531sub-0.449hy+0.207Threshold-0.145pssm-mut...

Eigenvectors
V1       V2       V3     V4      V5      V6 
-0.4716  0.4104  0.219  -0.0231  0.215   0.2071 Threshold
-0.153  -0.1263  0.6977  0.0049 -0.1865 -0.6556 vdw
 0.2465  0.4028 -0.1679  0.346   0.6055 -0.4486 hyd
 0.2511  0.1609  0.6277  0.3299  0.1513  0.5306 sub
 0.2799  0.5007  0.0529 -0.5097 -0.3189  0.061  pssm-wt
-0.335   0.593  -0.1004  0.0423 -0.2602 -0.145  pssm-mut
 0.5118  0.0616  0.1544 -0.4783  0.2354 -0.1246 ent
 0.4217  0.1459 -0.0799  0.5297 -0.5521 -0.0645 impRes

So, based on your answer, if now I choose V1 and V2 to represent my data, I have to use V1 and V2 and calculate the new attributes for the test set, previous to upload the test set to the model..

 V1 --> new att 1 = 0.512ent-0.472Threshold+0.422impRes-0.335pssm-mut+0.28pssm-wt...
 V2 --> new att 2 = 0.593pssm-mut+0.501pssm-wt+0.41Threshold+0.403hyd+0.161sub...

I didn't know this forum existed so I asked it stackoverflow and maybe it's more suitable here...

I have a question regarding dimensionality reduction using PCA.

If I have a train set (train.arff, 10 attributes) I perform a PCA and I save my data with respect to the new transformed variables (say I choose the two first attribues, combination of the original ones, that collect most of the variance), and call this transformed trainset "trainset-afterPCA.arff". Now I train a model using this file (which only has 2 attributes), and save it.

If now I have a new dataset, constructed with the original 10 attributes, and I want to use the model I built before to classify this new data, how do I have to proceed?

If I just try to test on this new dataset, train and test are'nt compatible, right? If I ran PCA on the test set, the resulting new attributes won't be the same as the ones obtained in the training set. What should I do?

Thanks in advance

I didn't know this forum existed so I asked this on Stack Overflow but maybe it's more suitable here...

I have a question regarding dimensionality reduction using PCA.

If I have a train set (train.arff, 10 attributes) I perform a PCA and I save my data with respect to the new transformed variables (say I choose the two first attributes, combination of the original ones, that collect most of the variance), and call this transformed trainset "trainset-afterPCA.arff". Now I train a model using this file (which only has 2 attributes), and save it.

If now I have a new dataset, constructed with the original 10 attributes, and I want to use the model I built before to classify this new data, how do I have to proceed?

If I just try to test on this new dataset, train and test aren't compatible, right? If I ran PCA on the test set, the resulting new attributes won't be the same as the ones obtained in the training set. What should I do?