Valid approach to generate new training data out of some existing training data

Question

Is there any valid approach to generate new training data out of some existing training data. I ask this question only in regard of my learning problem not in a general context.

My learning problem is to convert the 6 dimension acceleration and gyroscope data (data is obtained from a sensor attached to the user wrist while he/she try to writ some characters in the air) to the corresponding characters A to Z.

I have only 10 sample data for each character so in total 260 training instances.For example the following images are related to 2 sample data for character "F"

Also I have many sample data while user do not moves his hand and waiting for the next character to write but because of hand spontaneous and random movement still we have some pattern of data although compared to my 260 training data they are flat and they can be different from one user to another you can see it in the following image.

Also we can say that each training sample data some how is a mixture of an ideal hand movement for writing the character + these spontaneous and random movement of hand (something like noise)

I want to know if there is any way that I can generate some new training data. Maybe I can combine my current 260 training and the data from the time user do not move his hand to generate some new training data. Maybe one can say there is no need to generate new training data and I can just simply relay on my 10 sample data for each character then use for example k-NN algorithm to find the nearest neighbours (which I did and it looks promising, for example in the following image you can see the distance calculated with DTW from one unseen A character from my all 260 sample data) so then there is no need to extra training data.

Thanks

Answer 1

I dont think you need to create new data, you just need to use the data you have properly.

First trick: Using a subset of the six time series will get you higher accuracy than using all 6. This may be unintuitive, but it is true. See [a] or [b]. How do you find the best set? Greedy forward search works quite well.

Second trick: Setting the right warping window width will give you a significant improvement. Compare the second and third last columns of [c]

eamonn

[a] http://www.cs.ucr.edu/~eamonn/Multi-Dimensional_DTW_Journal.pdf [b] http://www.cs.ucr.edu/~eamonn/MultiD_ICDM_cameraReady.pdf

[c] UCR Time Series Classification Archive