Extreme Learning Machines. Tony Oakden ANU AI Masters Project (early Presentation) 4/8/2014
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1 Extreme Learning Machines Tony Oakden ANU AI Masters Project (early Presentation) 4/8/2014
2 This presentation covers: Revision of Neural Network theory Introduction to Extreme Learning Machines ELM Early Results Brief description of code Discuss possible future work
3 Neural Network Revision In a single layer perceptron inputs are connected to output nodes via weights Training is carried out using least squares or similar function Pros Simple and quick to train Cons Can only learn to classify linearly separable problems
4 Hidden Layer To classify none linear data we must add an additional layer of weights between input and output (hidden layer) When combined with a suitable activation function (sigmoidal for example) the network can classify none linear functions To train the hidden layer we propagate errors on the output back through the network. This is the back propagation algorithm Pros: Can theoretically classify any data set Cons Training of the network can be very slower
5 Extreme Learning Machines Provide a way to train networks to classify none linear problems without back propagation These networks still use a hidden layer. But the weights and bias in the hidden layer are set to random values We only train the output nodes. Training is achieved using least squares algorithm Pros: Very fast training time Cons: Less accurate
6 Wait, we use random weights? Huh? Sounds too good to be true so lets look at some results:
7 Two Spirals Data Set First set of experiments where carried out with the twin spiral data set. This was used because: It is a difficult set to classify Easy visualization of results
8 Neural Network trained with back propagation 20 nodes in hidden layer Training time is 6.4 seconds Training accuracy is 100% (Testing was performed with training data)
9 Extreme Learning Machine 20 nodes in hidden layer Training time is 0.02 seconds Training accuracy is 69% Not great But
10 Extreme Learning continued 200 nodes in hidden layer Training time is seconds Training accuracy is 97% If the number of nodes in the hidden layer is significantly increased then performance improves dramatically but time taken to train still remains much faster than a traditional network
11 Accuracy plotted against hidden Layer/ Chart Title
12 Matlab Code %create random weights for hidden layer InputWeight=rand(NumberofHiddenNeurons,NumberofInputNeurons)*2-1; BiasofHiddenNeurons=rand(NumberofHiddenNeurons,1);. temph=inputweight*traindata.p; ind=ones(1,numberoftrainingdata); BiasMatrix=BiasofHiddenNeurons(:,ind); temph=temph+biasmatrix; % Calculate hidden neuron output matrix H % Extend the bias matrix BiasofHiddenNeurons to match the dimention of H % we can use a variety of activation functions here but we ll stick to sigmoidal for now H = 1./ (1 + exp(-temph)); OutputWeight=pinv(H') * traindata.t'; % pinv gives Moore-Penrose pseudoinverse matrix
13 Conclusion As can be see training times for ELM are very fast. From these early experiments 100 times faster than traditional back prop for similar accuracy Accuracy is slightly lower, with other data sets back prop achieved 85% ELM 80%. But for many applications is still good enough Increasing the number of nodes in the hidden layer improves performance at the expense of a small increase in training time
14 Further research Use of ELM with GA for feature selection (this weeks work) Experiment with different data sets Perform more rigorous analysis of results So far we have only looked at binary classifiers. How does ELM algorithm cope with multi-class classification? Can we improve the accuracy of ELM in some way, maybe by combining results with cascade networks? What about continuous data sources? Second part of project is cascade networks, can these be combined with elm in some way?
15 references Guang-Bin Huang: An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels (Springer Science+Business Media New York 2014)
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