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Bayesian Compressive Sensing
TLDR
The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M of basis-function coefficients associated with B. Expand
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Multi-Task Learning for Classification with Dirichlet Process Priors
TLDR
We investigate the problem of learning logistic-regression models for multiple classification tasks, where the training data set for each task is not drawn from the same statistical distribution. Expand
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Semi-Supervised Classification
A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sampleExpand
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Logistic regression with an auxiliary data source
TLDR
We introduce an auxiliary variable μ for each example in <i>D<sup>a</sup></i> to reflect its mismatch with Dp and propose a method to correct the sample-selection bias. Expand
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The Matrix Stick-Breaking Process
In analyzing data from multiple related studies, it often is of interest to borrow information across studies and to cluster similar studies. Although parametric hierarchical models are commonlyExpand
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p-PIC: Parallel power iteration clustering for big data
TLDR
Power iteration clustering (PIC) is a newly developed clustering algorithm. Expand
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On Classification with Incomplete Data
TLDR
We address the incomplete-data problem in which feature vectors to be classified are missing data (features). Expand
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The matrix stick-breaking process for flexible multi-task learning
TLDR
We develop a new multitask-learning prior, termed the matrix stick-breaking process (MSBP), which encourages cross-task sharing of data. Expand
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Multitask Classification by Learning the Task Relevance
TLDR
We introduce a set of relevance parameters that control the degree to which data from other tasks are used in estimating the current task's classifier parameters. Expand
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Incomplete-data classification using logistic regression
TLDR
A logistic regression classification algorithm is developed for problems in which the feature vectors may be missing data (features). Expand
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