mindist

#MinDist

##Description The MinDist (Minimum Distance) algorithm is a simple classifier that works well on both basic and more complex recognition problems. The MinDist algorithm is a very fast classifier and so is a good choice if you need to a classifier with a low-computional overhead.

The MinDist algorithm is a supervised learning algorithm that can be used to classify any type of N-dimensional signal. The MinDist algorithm works by fitting M clusters to the data from each class during the training phase. A new sample is then classified by finding the class that has the cluster with the minimum distance (Euclidean) to the new sample. This makes the MinDist algorithm particularly fast at classifying new samples, compared with other classification algorithms such as KNN.

The MinDist algorithm also computes rejection thresholds that enable the algorithm to automatically reject sensor values that are not the K gestures the algorithm has been trained to recognized (without being explicitly told during the prediction phase if a gesture is, or is not, being performed).

The MinDist algorithm is part of the GRT classification modules.

##Advantages The MinDist algorithm is a very good algorithm to use for the classification of static postures and non-temporal pattern recognition. The MinDist algorithm is a particularly fast classifier.

##Disadvantages The main limitation of the MinDist algorithm is that choosing the "wrong" number of clusters may result in a poor classification result. The user may therefore want to train the algorithm with several different cluster values (i.e. 2, 5, 10, 100, etc.) and run cross validation to determine a "good" cluster value. A function to estimate a suitable cluster value will be added soon.

##Training Data Format You should use the ClassificationData data structure to train the MinDist classifier. Example Code

##Example MinDist Example