The 13th ICDM workshop on high dimensional data analysis

The ICDM Workshop on High Dimensional Data Analysis addresses the fundamental challenges of extracting meaningful patterns and knowledge from datasets with thousands or millions of features. As data collection in genomics, imaging, finance, and internet applications routinely produces high-dimensional datasets, developing algorithms that scale gracefully and avoid the curse of dimensionality has become a central concern in data mining and machine learning. The workshop covers dimensionality reduction, feature selection, sparse modeling, manifold learning, and applications of high-dimensional analysis to real-world problems co-located with the IEEE International Conference on Data Mining.

data mining machine learning artificial intelligence

Submissions are invited on dimensionality reduction methods, feature selection and extraction, sparse regression and classification, matrix and tensor factorization, manifold learning, high-dimensional clustering, distribution estimation in high dimensions, scalable algorithms for large feature spaces, and applications of high-dimensional data analysis in genomics, imaging, text mining, and financial data. Theoretical analyses and empirical studies are both welcome.