SVM

III. Support Vector Machines

Idea: Use a nonlinear mapping kernal to project data onto a higher dimension in order to find a separating hyperplane with the largest margin between the data.

Steps:

Preprocess data by first normalizing and scaling all data (between -1 and 1).
Use a Gaussian kernel to project data onto a higher dimension.
Use leave-one-out validation to train and find a transformation so that the number of support vectors for each class is small to minimize expected error rate.
Classify using SVM.

.Results.

	Upright	Leaning Forward	Leaning Left	Leaning Right	Leaning Back	Slouching	% Recognition
Upright	84	8	0	0	8	0	84%
Leaning Forward	1	99	0	0	0	0	99%
Leaning Left	0	0	100	0	0	0	100%
Leaning Right	0	0	0	100	0	0	100%
Leaning Back	2	0	0	0	95	3	95%
Slouching	0	0	0	0	1	99	99%
						Total Recognition	96.17%

Table 1: Confusion Matrix for SVM using leave-one-out cross validation

	Upright	Leaning Forward	Leaning Left	Leaning Right	Leaning Back	Slouching	% Recognition
Upright	82	12	0	2	4	0	82%
Leaning Forward	0	100	0	0	0	0	100%
Leaning Left	6	6	88	0	0	0	88%
Leaning Right	24	12	0	64	0	0	64%
Leaning Back	10	0	0	0	90	0	90%
Slouching	0	0	0	0	38	62	62%
						Total Recognition	81.00%

Table 2: Confusion Matrix for SVM with 10 new subjects

.Other Explored Areas.

Results using linear classifier:

cross validation = 94%

10 new subjects = 80.33%

Results using mixture model parameters as input:

cross validation = 47.50%

10 new subjects = 41.33%