I have an autoencoder input of 100x21. The 21 columns are PC scores, the 100 rows are observations. The importance of the columns degrades as the column number increases. The first column is the most important for the data variance, the last column is the least important. To be able to reconstruct the data back from PCA the first columns need to be as correct as possible.
I have tried searching whether I can adjust weights or something else of the autoencoder layers to include this importance of the columns, but I have not found it.
In other words, I want errors in the first (e.g 5) columns to be punished more harshly than errors in the last (e.g 5) columns.
I would be grateful if someone could point me in the right direction!
Iljaaaa t1_j4uub0z wrote
Reply to [D] Simple Questions Thread by AutoModerator
I have an autoencoder input of 100x21. The 21 columns are PC scores, the 100 rows are observations. The importance of the columns degrades as the column number increases. The first column is the most important for the data variance, the last column is the least important. To be able to reconstruct the data back from PCA the first columns need to be as correct as possible.
I have tried searching whether I can adjust weights or something else of the autoencoder layers to include this importance of the columns, but I have not found it.
In other words, I want errors in the first (e.g 5) columns to be punished more harshly than errors in the last (e.g 5) columns.
I would be grateful if someone could point me in the right direction!