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Exploring Facial Features for Classification Tasks

This study examines the impact of glasses and beard distinctions on classification accuracy using advanced learning techniques and data representation methods. It delves into the analysis of multiple attributes to infer missing labels in a semi-supervised setting, emphasizing the importance of manifold structure shared across datasets. The research also explores the classification of objects based on features and labels, with a focus on digit recognition and computer vision applications.

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Exploring Facial Features for Classification Tasks

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  1. Different Features

  2. Glasses vs. No Glasses

  3. Beard vs. No Beard

  4. Beard Distinction Ghodsi et, al 2007

  5. Glasses Distinction Ghodsi et, al 2007

  6. Multiple-Attribute Metric Ghodsi et, al 2007

  7. Embedding of sparse music similarity graph Platt, 2004

  8. Reinforcement learning Mahadevan and Maggioini, 2005

  9. Semi-supervised learning Use graph-based discretization of manifold to infer missing labels. Belkin & Niyogi, 2004; Zien et al, Eds., 2005 Build classifiers from bottom eigenvectors of graph Laplacian.

  10. http://www.bushorchimp.com correspondences

  11. c et al, 2003, 2005 Learning correspondences How can we learn manifold structure that is shared across multiple data sets?

  12. Mapping and robot localization • Bowling, Ghodsi, Wilkinson 2005 Ham, Lin, D.D. 2005

  13. Classification

  14. Classification

  15. Data

  16. Features (X) (Green, 6, 4, 4.5) (Green, 7, 4.5, 5) (Red, 6, 3, 3.5) (Red, 4.5, 4, 4.5) (Yellow, 1.5, 8, 2) (Yellow, 1.5, 7, 2.5)

  17. Data Representation

  18. Data Representation

  19. Data Representation

  20. Features and labels Green Pepper (Green, 6, 4, 4.5) (Green, 7, 4.5, 5) Green Pepper (Red, 6, 3, 3.5) Red Pepper (Red, 4.5, 4, 4.5) Red Pepper (Yellow, 1.5, 8, 2) Hot Pepper (Yellow, 1.5, 7, 2.5) Hot Pepper

  21. Features and labels Objects Features (X) Labels (Y)

  22. Classification (New point) h(Red, 7, 4, 4.5) (Red, 7, 4, 4.5) ?

  23. Classification (New point) h(Red, 5, 3, 4.5) (Red, 5, 3, 4.5) ?

  24. Digit Recognition

  25. Classification

  26. Classification

  27. Classification

  28. Classification

  29. Computer Vision N. Jojic and B.J. Frey, “ Learning flexible sprites in video layers”, CVPR 2001, (Video)

  30. Reading • Journals: Neural Computation, JMLR, ML, IEEE PAMI • Conferences: NIPS, UAI, ICML, AI-STATS, IJCAI, IJCNN • Vision: CVPR, ECCV, SIGGRAPH • Speech: EuroSpeech, ICSLP, ICASSP • Online: citesser, google • Books: • Elements of Statistical Learning, Hastie, Tibshirani, Friedman • Learning from Data, Cherkassky, Mulier • Pattern classification, Duda, Hart, Stork • Neural Networks for pattern Recognition, Bishop • Pattern recognition and machine learning, Bishop

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