20 research outputs found
DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropies
We introduce an information-theoretic quantity with similar properties to
mutual information that can be estimated from data without making explicit
assumptions on the underlying distribution. This quantity is based on a
recently proposed matrix-based entropy that uses the eigenvalues of a
normalized Gram matrix to compute an estimate of the eigenvalues of an
uncentered covariance operator in a reproducing kernel Hilbert space. We show
that a difference of matrix-based entropies (DiME) is well suited for problems
involving the maximization of mutual information between random variables.
While many methods for such tasks can lead to trivial solutions, DiME naturally
penalizes such outcomes. We compare DiME to several baseline estimators of
mutual information on a toy Gaussian dataset. We provide examples of use cases
for DiME, such as latent factor disentanglement and a multiview representation
learning problem where DiME is used to learn a shared representation among
views with high mutual information
A PERFORMANCE NARRATIVA DE UMA BLOGUEIRA: "TORNANDO-SE PRETA EM UM SEGUNDO NASCIMENTO"
A web 2.0 propicia aos sujeitos sociais a possibilidade de contar suas histórias assim como de vê-las discutidas em novas formas de interação. Este artigo almeja apresentar os posicionamentos interacionais que constroem a performance narrativa de co-construção de raça de uma mulher negra no blog "Eu, Mulher Preta". O estudo se ampara nos aportes teóricos dos novos letramentos digitais, na concepção de raça proposta pelas Teorias Queer e na teorização de narrativa como performance. Para analisar a narrativa da blogueira como performance, o quadro analítico se ancora no construto de posicionamento interacional e nas pistas que marcam tal posicionamento na encenação da performance. Os resultados indicam que a narradora se posiciona interacionalmente como mulher preta. Identificamos, porém, um posicionamento interacional anterior ao renascimento como negra: o de mulher "branc[a] meio suj[a]". Observamos ainda que tais posicionamentos refletem duas performances discursivas conflitantes, uma que se aproxima e valoriza a negritude e outra que se distancia de sua origem. Esta investigação, baseando-se nas Teorias Queer, possibilita, também, tratar a questão racial como um traço performativo, colocando-a ao lado de gênero e sexualidade
A GREEDY ALGORITHM FOR MODEL SELECTION OF TENSOR DECOMPOSITIONS
Various tensor decompositions use different arrangements of factors to explain multi-way data. Components from different decompositions can vary in the number of parameters. Allowing a model to contain components from different decompositions results in a combinatoric number of possible models. We consider model selection to balance approximation error with the number of parameters, but due to the number of possibilities post-hoc model selection is infeasible. Instead, we incrementally build a model. This approach is analogous to sparse coding with a union of dictionaries. The proposed greedy approach can estimate a model consisting of a combination of tensor decompositions. Index Terms — tensor decompositions, greedy algorithm, model selection 1
The Representation Jensen-R\'enyi Divergence
We introduce a divergence measure between data distributions based on
operators in reproducing kernel Hilbert spaces defined by kernels. The
empirical estimator of the divergence is computed using the eigenvalues of
positive definite Gram matrices that are obtained by evaluating the kernel over
pairs of data points. The new measure shares similar properties to
Jensen-Shannon divergence. Convergence of the proposed estimators follows from
concentration results based on the difference between the ordered spectrum of
the Gram matrices and the integral operators associated with the population
quantities. The proposed measure of divergence avoids the estimation of the
probability distribution underlying the data. Numerical experiments involving
comparing distributions and applications to sampling unbalanced data for
classification show that the proposed divergence can achieve state of the art
results.Comment: We added acknowledgment
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Locating Spatial Patterns of Waveforms during Sensory Perception in Scalp EEG
The spatio-temporal oscillations in EEG waves are indicative of sensory and cognitive processing. We propose a method to find the spatial amplitude patterns of a time- limited waveform across multiple EEG channels. It consists of a single iteration of multichannel matching pursuit where the base waveform is obtained via the Hilbert transform of a time-limited tone. The vector of extracted amplitudes across channels is used for classification, and we analyze the effect of deviation in temporal alignment of the waveform on classification performance. Results for a previously published dataset of 6 subjects show comparable results versus a more complicated criteria-based method.