Corpus ID: 16509056

Learning Non-Linear Feature Maps

@article{Athanasakis2013LearningNF,
  title={Learning Non-Linear Feature Maps},
  author={Dimitris Athanasakis and J. Shawe-Taylor and D. Fernandez-Reyes},
  journal={ArXiv},
  year={2013},
  volume={abs/1311.5636}
}
Feature selection plays a pivotal role in learning, particularly in areas were parsimonious features can provide insight into the underlying process, such as biology. Recent approaches for non-linear feature selection employing greedy optimisation of Centred Kernel Target Alignment(KTA), while exhibiting strong results in terms of generalisation accuracy and sparsity, can become computationally prohibitive for high-dimensional datasets. We propose randSel, a randomised feature selection… Expand
2 Citations
Materials Classification Using Sparse Gray-Scale Bidirectional Reflectance Measurements
  • 1
  • PDF

References

SHOWING 1-10 OF 17 REFERENCES
Feature Selection via Dependence Maximization
  • 281
  • Highly Influential
  • PDF
Fast dependency-aware feature selection in very-high-dimensional pattern recognition
  • 14
  • PDF
Algorithms for Learning Kernels Based on Centered Alignment
  • 296
  • PDF
Stability Selection
  • 1,600
  • PDF
On Kernel-Target Alignment
  • 994
  • PDF
Measuring Statistical Dependence with Hilbert-Schmidt Norms
  • 949
  • PDF
Multiple Kernel Learning on the Limit Order Book
  • 30
  • PDF
Gene Selection for Cancer Classification using Support Vector Machines
  • 4,759
  • PDF
Challenges in representation learning: A report on three machine learning contests
  • 551
  • PDF
...
1
2
...