Spatio-Temporal Self Organising Map

• US 20080288493A1
• Filed: 03/16/2006
• Published: 11/20/2008
• Est. Priority Date: 03/16/2005
• Status: Abandoned Application

• Current Assignee: Imperial Innovations Limited (Imperial College London)
• Original Assignee: Imperial Innovations Limited (Imperial College London)
• Inventors: Yang, Guang-Zhong, Lai Lo, Benny Ping, Thiemjarus, Surapa
• Application Number:
  US11/886,241
• Publication Number:
  US 20080288493A1
• Time in Patent Office: Days
• US Class Current: 707/7
• CPC Class Codes: G06F 18/2137 based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G06V 10/94 Hardware or software architectures specially adapted for image or video understanding
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

A method of classifying a data record as belonging to one of a plurality of classes, the data records comprising a plurality of data samples, each sample comprising a plurality of features derived from a value sampled from a sensor signal at a point in time, the method including: defining a selection variable indicative of the temporal variation of the sensor signals within a time window; defining a selection criterion for the selection variable; comparing a value of the selection variable to the selection criterion to select an input representation for a self organising map, the map having a plurality of input and output units, and deriving an input from the data samples within the time window in accordance with the selected input representation; and applying the input to a self organising map corresponding to the selected input representation and classifying the data record based on a winning output unit of the self organising map.

Claims

1. A method of classifying a data record as belonging to one of a plurality of classes, the data records comprising a plurality of data samples, each sample comprising a plurality of features derived from a value sampled from a sensor signal at a point in time, the method including:

(a) defining a selection variable indicative of the temporal variation of the sensor signals within a time window;

(b) defining a selection criterion for the selection variable;

(c) comparing a value of the selection variable to the selection criterion to select an input representation for a self organising map, the map having a plurality of input and output units, and deriving an input from the data samples within the time window in accordance with the selected input representation; and

(d) applying the input to a self organising map corresponding to the selected input representation and classifying the data record based on a winning output unit of the self organising map.

2. A method as claimed in claim 1, the selection variable being a measure of the variability of the output units of the self organising map calculated over the time window.

3. A method as claimed in claim 2, the selection variable being a normalised entropy of a probability distribution over winning output units calculated over the time window, a value of the probability distribution for a winning output unit being a number of samples for which said output unit is a winning output unit divided by a number of samples in the time window.

4. A method as claimed in claim 1, the method including applying data samples of a time window to a first map and using the output of the first map to calculate the selection variable;

the method further comprising deciding based on the selection variable whether to use the first map or a second map for classifying the data.

5. A method as claimed in claim 1, the selection variable being a measure of the temporal variability of the data samples.

6. A method as claimed in claim 1, the selection criterion comprising a threshold or a decision surface distinguishing static and dynamic data records, the static data records being sampled from a sensor signal having a substantially constant statistical distribution and the dynamic data records being sampled from a sensor signal having a time varying statistical distribution.

7. A method as claimed in claim 6, the input representation for a data record determined to be a dynamic data set comprising an average peak duration calculated over a set of features as the number of features in the set divided by the sum of the number of local extreme values of each feature within the time window.

8. A method as claimed in claim 6, the input representation for a data record determined to be a dynamic data record comprising an average peak area calculated for each feature, calculated as the sum over all records in the time window of the absolute difference between the value of each respective feature of each record and the average value of that feature calculated over all records within the time window, divided by the number of extreme values within the time window.

9. A method as claimed in claim 1 in which classifying the data record includes:

e) looking up an associated map associated with the winning output unit in a table associating maps with output units or labels associated with output units;

f) if the associated map is the said self-organising map, classifying the data record using a label associated with the winning output unit; and

otherwise

g) applying the data record to the associated map and classifying it based on a winning output unit of that map.

10. A system adapted to implement a method as claimed in claim 1.

11. A system as claimed in claim 10, the system comprising a plurality of sensor/processing units, each unit comprising, one or more sensors and a selector arranged to define a selection variable indicative of the temporal variation of the sensor signals within a time window and a selection criterion for the selection variable, the selector further being arranged to compare a value of the selection variable to the selection criterion to select an input representation for a self organising map, the map having a plurality of input and output units and deriving an input from the data records within the time window in accordance with the selected input representation;

the unit further comprising an interface for applying the input to a self organising map corresponding to the input representation and a transmitter for transmitting the output of said self organising map to a central processor.

12. A computer readable medium carrying a computer program comprising computer code instructions for implementing a method as claimed in claim 1.

13. An electromagnetic signal representative of a computer program comprising computer code instructions for implementing a method as claimed in claim 1.

14. A method of training a classifier for classifying a data record as belonging to one of a plurality of classes, the data record comprising a plurality of data samples and each sample comprising a plurality of features derived from a value sampled from a sensor signal at a point in time, the method including:

(a) computing a derived representation representative of a temporal variation of the features of a dynamic data record within a time window;

(b) using the derived representation as an input for a second self-organised map; and

(c) updating the parameters of the self-organised map according to a training algorithm.

15. A method of training a classifier as claimed in claim 14, the method including sampling a plurality of samples from a plurality of static and dynamic records belonging to a plurality of classes;

using the said samples as an input for a first self organised map;

calculating a measure of temporal variability of the samples within each record; and

partitioning the plurality of records into static and dynamic records based on said measure.

16. A method of training a classifier, in particular as claimed in claim 14, including calculating a confusion matrix for a plurality of classes associated with output units of a self-organised map for a plurality of labelled data records;

clustering together classes which are determined to be confused into confused clusters associating each of the classes of a confused cluster with a further self-organised map and using those data records labelled as belonging to a class of a particular confused cluster as an input to a corresponding further self-organised map to train it.