Machine learning, classification type

Question

I am studying for my Machine Learning (ML) class and I have question that I couldn't find an answer with my current knowledge. Assume that I have the following data-set,

att1 att2 att3 class
5    6    10   a
2    1    5    b
47   8    4    c
4    9    8    a
4    5    6    b

The above data-set is clear and I think I can apply classification algorithms for new incoming data after I train my data-set. Since each instance has a label, it is easy to understand that each instance has a class that is labeled with. Now, my question is what if we had a class consisting of different instances such as gesture recognition data. Any class will have multiple instances that specifies its class. For example,

xcor ycord depth 
45   100   10
50   20    45
10   51    12

the above three instances belong to class A and the below three instances belong to class B as a group, I mean those three data instances constitute that class together. For gesture data, the coordinates of movement of your hand.

xcor ycord depth 
45   100   10
50   20    45
10   51    12

Now, I want every incoming three instances to be grouped either as A or B? Is it possible to label all of them either A or B without labeling each instance independently? As an example, assume that following group belongs to B, so I want all of the instances to be labelled together as B not individually because of their independent similarity to class A or B? If it is possible, how do we call it?

xcor ycord depth 
45   10    10
5    20    87
10   51    44

Answer 1

I don't see an scenario where you might want to group an indeterminate number of rows in your dataset as features of a given class. They are either independently associated with a class or they are all features and therefore an unique row. Something like:

Instead of
xcor ycord depth 
45   10    10
5    20    87
10   51    44

Would be something like:

xcor1 ycord1 depth1 xcor2 ycord2 depth2 xcor3 ycord3 depth3  
45      10    10      5    20     87     10     51    44

This is pretty much the same approach that is used to model time series

Answer 2

It seems you may be confused between different types of machine learning.

The dataset given in your class is an example of a supervised classification algorithm. That is, given some data and some classes, learn a classifier that can predict classes on new, unseen data. Classifiers that you can apply to this problem include

• decision trees,
• support vector machines
• artificial neural networks, etc.

The second problem you are describing is an example of an unsupervised classification problem. That is, given some data without labels, we want to find an automatic way to separate the different types of data (your A and B) algorithmically. Algorithms that solve this problem include

• K-means clustering
• Mixture models
• Principal components analysis followed by some sort of clustering

I would look into running a factor analysis or normalizing your data, then running a K-means or gaussian mixture model. This should discover the A and B types of your data if they are distinguishable.

Answer 3

Take a peek at the use of neural networks for recognizing hand-written text. You can think of a gesture as a hand-written figure with an additional time component (so, give each pixel an "age".) If your training data also includes similar time data, then I think the technique should carry over well.