Time series are difficult to monitor, summarize and predict. Segmentation organizes time series into few intervals having uniform characteristics (flatness, linearity, modality,...
Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
The secure multi-party computation (SMC) model provides means for balancing the use and confidentiality of distributed data. Increasing security concerns have led to a surge in w...
The paper introduces a notion of support for realvalued functions. It is shown how to approximate supports of a large class of functions based on supports of so called polynomial ...
We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace...
Individuals show different cell classes when they are in the different stages of a disease, have different disease subtypes, or have different response to a treatment or envir...
An increasing amount of data is produced in the form of text streams − these can be RSS news feeds, TV closed captions, emails, etc. We study the problem of answering keyword qu...
Vagelis Hristidis, Oscar Valdivia, Michail Vlachos...
k-anonymity is a popular measure of privacy for data publishing: It measures the risk of identity-disclosure of individuals whose personal information are released in the form of ...
Bijit Hore, Ravi Chandra Jammalamadaka, Sharad Meh...