Scalable and Privacy-Preserving Federated Principal Component Analysis
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Billions of people now have conversations daily over the Internet. A large portion of this communication takes place via secure messaging protocols that offer "end-to-end encryption'" guarantees and resilience to compromise like the widely-used Double Ratc ...
Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PC ...
Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...
JMLR-JOURNAL MACHINE LEARNING RESEARCH2022
This paper introduces Memory-limited Online Subspace Estimation Scheme (MOSES) for both estimating the principal components of streaming data and reducing its dimension. More specifically, in various applications such as sensor networks, the data vectors a ...
IEEE COMPUTER SOC2020
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Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimensional spaces. A major disadvantage of kernel methods is their poor scalabili ...
ASSOC COMPUTING MACHINERY2020
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We give an information-theoretic interpretation of Canonical Correlation Analysis (CCA) via (relaxed) Wyner's common information. CCA permits to extract from two high-dimensional data sets low-dimensional descriptions (features) that capture the commonalit ...
IEEE2020
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In the context of next-generation radio interferometers, we are facing a big challenge of how to economically process data. The classical dimensionality reduction technique, averaging visibilities on time, may dilute fast radio transients (FRT). We propose ...
2019
Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by mor ...
Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dime ...
Linear subspace models are pervasive in computational sciences and particularly used for large datasets which are often incomplete due to privacy issues or sampling constraints. Therefore, a critical problem is developing an efficient algorithm for detecti ...
