Journal ArticleDOI
Pattern Recognition and Machine Learning
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TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
Citations
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Predicting Epileptic Seizures in Scalp EEG Based on a Variational Bayesian Gaussian Mixture Model of Zero-Crossing Intervals
TL;DR: A novel patient-specific seizure prediction method based on the analysis of positive zero-crossing intervals in scalp electroencephalogram (EEG) based on a variational Bayesian Gaussian mixture model of the data is proposed.
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Multimodal Task-Driven Dictionary Learning for Image Classification
TL;DR: This paper proposes a multimodal task-driven dictionary learning algorithm under the joint sparsity constraint (prior) to enforce collaborations among multiple homogeneous/heterogeneous sources of information and presents an extension of the proposed formulation using a mixed joint and independent sparsity prior, which facilitates more flexible fusion of the modalities at feature level.
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
TL;DR: This work presents MultiPath, which leverages a fixed set of future state-sequence anchors that correspond to modes of the trajectory distribution, which is efficient, requiring only one forward inference pass to obtain multi-modal future distributions, and the output is parametric, allowing compact communication and analytical probabilistic queries.
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Analysis of Multimodal Neuroimaging Data
TL;DR: A comprehensive overview of mathematical tools reoccurring in multimodal neuroimaging studies for artifact removal, data-driven and model-driven analyses, enabling the practitioner to try established or new combinations from these algorithmic building blocks.
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Cloud removal in remote sensing images using nonnegative matrix factorization and error correction
TL;DR: Compared with other cloud removal methods, the results demonstrate that S-NMF-EC is visually and quantitatively effective for the removal of thick clouds, thin clouds, and shadows.