Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning

SIAM 2013
Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning
Ayan Acharya, Eduardo R.Hruschka, Joydeep Ghosh, Badrul Sarwar, Jean-David Ruvini

Unsupervised models can provide supplementary soft constraints to help classify new target data under the assumption that similar objects in the target set are more likely to share the same class label. Such models can also help detect possible dierences between training and target distributions,

which is useful in applications where concept drift may take place. This paper describes a Bayesian frame work that takes as input class labels from existing classefiers (designed based on labeled data from the source domain),

as well as cluster labels from a cluster ensemble operating solely on the target data to be classified and yields a con-ensus labeling of the target data. This framework is particularly useful when the statistics of the target data drift or change from those of the training data.

We also show that the proposed framework is privacy-aware and allows performing distributed learning when data/models have sharing restrictions. Experiments show that our framework can yield superior results to those provided by applying classifier ensembles only.

Another publication from the same category: Machine Learning and Data Science

IEEE Computing Conference 2018, London, UK

Regularization of the Kernel Matrix via Covariance Matrix Shrinkage Estimation

The kernel trick concept, formulated as an inner product in a feature space, facilitates powerful extensions to many well-known algorithms. While the kernel matrix involves inner products in the feature space, the sample covariance matrix of the data requires outer products. Therefore, their spectral properties are tightly connected. This allows us to examine the kernel matrix through the sample covariance matrix in the feature space and vice versa. The use of kernels often involves a large number of features, compared to the number of observations. In this scenario, the sample covariance matrix is not well-conditioned nor is it necessarily invertible, mandating a solution to the problem of estimating high-dimensional covariance matrices under small sample size conditions. We tackle this problem through the use of a shrinkage estimator that offers a compromise between the sample covariance matrix and a well-conditioned matrix (also known as the "target") with the aim of minimizing the mean-squared error (MSE). We propose a distribution-free kernel matrix regularization approach that is tuned directly from the kernel matrix, avoiding the need to address the feature space explicitly. Numerical simulations demonstrate that the proposed regularization is effective in classification tasks.