Nonparametric estimation and learning with applications to object recognition and image analysis

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Rapid advances in computer technologies for processing visual information create constant demand for new image representation and classification techniques. There is rapidly growing number of applications where data mining and classification play key role. Biometrics is one example where face images, fingerprints or retina scans are used to provide secure access to protected information or to identify an individual. Automatic categorization of pathological cells and cancer malignancy grading is another application where reliable classification systems are needed. Extensive research in machine learning in the last decade demonstrated that good features with strong discriminative power and careful tuning of classifiers lead to significant improvements in classifiers performance. This proposal builds on my earlier work investigating asymptotic properties of various nonparametric classification algorithms. The research that I propose is aimed at automatic extraction of "strong" features providing good separation of different categories and at designing better classifiers by carefully choosing their parameters in data-dependent way and by investigating their rates of convergence. Knowledge of the rates of convergence will allow the designer to choose the best classifiers in a given application domain and is important for assessing computational complexity of the classification systems. I will investigate new techniques for dimensionality reduction using principal surfaces in high dimensional spaces and I will refine automatic feature selection by means of convolutional neural networks. The results of this research will be applied to automatic face and handwriting recognition and malignancy grading of cancer cells.