ML-ELM

ML-ELM is a batch model that stacks learned ELM-AE feature maps and trains a ridge classifier on the final representation.

Dataflow

API

feature_elm::MlElm<float> model(
    numInputs,
    {64, 32},
    feature_elm::ActivationFunction::kSigmoid,
    feature_elm::Backend::kCpu,
    1e-6f,
    42u);

model.train(trainData, trainTargets, numSamples, numOutputs);
auto predictions = model.predictBatch(testData, testSamples);

MlElm owns a StackedFeatureMap and a BatchRidgeSolver. The feature stack is fitted greedily layer by layer during train; the final classifier is trained once on the final features.

When to use

• The target function is nonlinear and not well captured by one additive ELM layer.
• You want learned feature extraction without backpropagation.
• Batch training is acceptable.

Comparison baseline

Compare against:

• Batch ELM with the same number of final features.
• RBF features when local center geometry is expected.
• H-OS-ELM when the final head must be online.

Practical tips

• Keep the first layer at least as wide as the input for reconstruction-heavy tasks.
• Use a smaller second layer to force a compact representation.
• Increase ridgeAlpha if reconstruction or classifier solve is unstable.