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I'm Having a ML problem where my data set contains 60 features labelled into 3 groups (0, 1, -1).

I want to plot the data on a 2D surface to see how "close" (similar) data with label x is to data with label y, how the data spreads, are the labels separable, etc.

I was thinking about using PCA and transform the data from 80D to 2D, but It only retain 40% of the variance!

• Is this a good approach for the problem?
• If so, does 40% suffice?
• Are there any other/better approach for this?

EDIT:

Plotting is not the main issue. The transformation from 80D to 2D (for an easy visialization) is whats difficult.

Also, all of this is being made to know how much samples with label 1 differs from label 0 and label -1 and vice versa (based on those original 80 features).

If there's a different method, that is not visualizing the "answer", I'll also be happy to hear about it!

I'm Having a ML problem where my data set contains 80 features labelled into 3 groups (0, 1, -1).

I want to plot the data on a 2D surface to see how "close" (similar) data with label x is to data with label y, how the data spreads, are the labels separable, etc.

I was thinking about using PCA and transform the data from 80D to 2D, but It only retain 40% of the variance!

• Is this a good approach for the problem?
• If so, does 40% suffice?
• Are there any other/better approach for this?

EDIT:

Plotting is not the main issue. The transformation from 80D to 2D (for an easy visialization) is whats difficult.

Also, all of this is being made to know how much samples with label 1 differs from label 0 and label -1 and vice versa (based on those original 80 features).

If there's a different method, that is not visualizing the "answer", I'll also be happy to hear about it!

I'm Having a ML problem where my data set contains 60 features labelled into 3 groups (0, 1, -1).

I want to use K-means on that data and plot it on a 2D plot to see how close data with label x to data with label y

I was thinking about using PCA and transform the data from 60D to 2D, but It only retain 60% of the variance!

• Is this a good approach for the problem?
• If so, does 60% suffice?
• Are there any other/better approach for this?

EDIT:

Plotting is not the main issue. The transformation from 60D to 2D (for an easy visialization) is whats difficult.

Also, all of this is being made to know how much samples with label 1 differs from label 0 and label -1 and vice versa (based on those original 60 features).

If there's a different method, that is not visualizing the "answer", I'll also be happy to hear about it!

I'm Having a ML problem where my data set contains 60 features labelled into 3 groups (0, 1, -1).

I want to plot the data on a 2D surface to see how "close" (similar) data with label x is to data with label y, how the data spreads, are the labels separable, etc.

I was thinking about using PCA and transform the data from 80D to 2D, but It only retain 40% of the variance!

• Is this a good approach for the problem?
• If so, does 40% suffice?
• Are there any other/better approach for this?

EDIT:

Plotting is not the main issue. The transformation from 80D to 2D (for an easy visialization) is whats difficult.

Also, all of this is being made to know how much samples with label 1 differs from label 0 and label -1 and vice versa (based on those original 80 features).

If there's a different method, that is not visualizing the "answer", I'll also be happy to hear about it!

I'm Having a ML problem where my data set contains 60 features labelled into 3 groups (0, 1, -1).

I want to use K-means on that data and plot it on a 2D plot to see how close data with label x to data with label y

I was thinking about using PCA and transform the data from 60D to 2D, but It only retain 60% of the variance!

1. Is this a good approach for the problem?
2. If so, does 60% suffice?
3. Are there any other/better approach for this?

I'm Having a ML problem where my data set contains 60 features labelled into 3 groups (0, 1, -1).

I want to use K-means on that data and plot it on a 2D plot to see how close data with label x to data with label y

I was thinking about using PCA and transform the data from 60D to 2D, but It only retain 60% of the variance!

• Is this a good approach for the problem?
• If so, does 60% suffice?
• Are there any other/better approach for this?

EDIT:

Plotting is not the main issue. The transformation from 60D to 2D (for an easy visialization) is whats difficult.

Also, all of this is being made to know how much samples with label 1 differs from label 0 and label -1 and vice versa (based on those original 60 features).

If there's a different method, that is not visualizing the "answer", I'll also be happy to hear about it!