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Probabilistic archetypal analysis
This paper revisits archetypal analysis from the basic principles, and proposes a probabilistic framework that accommodates other observation types such as integers, binary, and probability vectors that corroborate the proposed methodology with convincing real-world applications. Expand
A Unified Framework for Quadratic Measures of Independence
It is shown that by generalizing the inner product using a symmetric strictly positive-definite kernel and by choosing appropriate kernels, it is possible to reproduce a number of popular measures of independence. Expand
A test of independence based on a generalized correlation function
This paper introduces the novel concept of parametric correntropy and design a test of independence based on it, and discusses how the proposed test relaxes the assumption of Gaussianity. Expand
Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences
This work explores strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate. Expand
Variable Selection: A Statistical Dependence Perspective
  • S. Seth, J. Príncipe
  • Mathematics, Computer Science
  • Ninth International Conference on Machine…
  • 12 December 2010
This paper discusses the properties of dependence as proposed by Renyi, and evaluates their significance in the variable selection context, and explores a measure of dependence that satisfies most of these desired properties, and discusses its applicability as a substitute for correlation coefficient and mutual information. Expand
Archetypal Analysis for Nominal Observations
  • S. Seth, M. Eugster
  • Mathematics, Computer Science
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 May 2016
This article views archetypal analysis in a generative framework: this allows explicit control over choosing a suitable number of archetypes by assigning appropriate prior information, and finding efficient update rules using variational Bayes'. Expand
Modelling-based experiment retrieval: A case study with gene expression clustering
A general probabilistic framework is introduced, where each experiment is modelled separately and the retrieval is done by finding related models, which induces a clustering of genes that show similar expression patterns across a number of samples. Expand
Signal Processing with Echo State Networks in the Complex Domain
The echo state network (ESN) approach for complex domain signal processing is proposed, which replaces the real connection weights for the reservoir and readout with complex numbers and the real activation functions with fully complex nonlinearities. Expand
Model Criticism in Latent Space
A method for latent variable models by pulling back the data into the space of latent variables, and carrying out model criticism in that space enables a more direct assessment of the assumptions made in the prior and likelihood. Expand
Exploration and retrieval of whole-metagenome sequencing
A content-based exploration and retrieval method for whole-metagenome sequencing samples using a distributed string mining framework to efficiently extract all informative sequence k-mers from a pool of metagenomic samples and use them to measure the dissimilarity between two samples. Expand