Wednesday, April 14, 2021


Data, including observations, measurements or images are quantized/characterized with certain feature representations in the digital world for further processing. However, there exists no a universal way to well-depict all the instances. Particularly, the optimal data descriptors often vary from class to class. We are thus motivated to fuse multiple kernel learning (MKL) into the training procedure, and carry out a class-specific feature selection framework, which significantly facilitates the relevant tasks, such as clustering and classification.


Multiple Kernel Learning with Local Learning

  • We address two unfavorable issues of local learning, i.e., high risk of overfitting and heavy computational cost, and present an efficient boosting algorithm to learn sample-specific local classifiers for object category recognition.
  • Our approach
    • We cast the multiple, independent training processes of local classifiers as a correlative multi-task learning problem.
    • We establish a parametric space where these local classifiers lie and spread as a manifold-like structure.
    • By designing a new multi-task boosting algorithm, the local classifiers are obtained by completing the manifold embedding.
    • The algorithm carries out incremental multiple kernel learning.


Cluster-dependent Feature Selection

  • A chicken-and-egg problem.
    • Clustering VS. Feature selection: Is it the clustering that contributes to the feature selection, or the feature selection that boosts the clustering?
  • Again, the optimal data descriptors often vary from cluster to cluster.
  • With the idea of associating each cluster with a learnable ensemble kernel, we integrate multiple kernel learning into the clustering procedure, and cast it as a joint optimization problem.




Cluster-dependent Feature Selection by Multiple Kernel Self-organizing Map

Kuan-Chieh Huang, Yen-Yu Lin, and Jie-Zhi Cheng

IEEE International Conference on Pattern Recognition (ICPR), November 2012



Clustering Complex Data with Group-dependent Feature Selection

Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh

European Conference on Computer Vision (ECCV), September 2010




Efficient Discriminative Local Learning for Object Recognition

Yen-Yu Lin, Jyun-Fan Tsai, and Tyng-Luh Liu

IEEE International conference on Computer Vision (ICCV), September 2009