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The sufficiency principle is the guiding principle for data reduction in statistical inference. There has been recent effort in developing the sufficiency principle for decentralized inference with a particular emphasis on the relationship between global sufficiency and local sufficiency. This paper studies the sufficiency based data reduction in tandem fusion systems when quantization is needed....
Scene semantic parsing is a challenging problem in the field of computer vision. Most approaches exploit low-level features to describe the whole scene. However, there is a large semantic gap between low-level features and high-level scene semantic. In this paper, a scene classification approach is proposed by exploiting semantic objects/materials of the background to reduce the semantic gap. The...
The ISAR (inverse synthetic aperture radar) imaging technology is an important tool for the ballistic missile midcourse target recognitions. Considering the rotationally symmetric targets, the sparse representation model of the ballistic midcourse targets with micro-motion is established. The sparse recovery algorithm named SBL (Sparse Bayesian Learning) is analyzed, which can provide a much sparser...
In this paper we present a set of theoretical results regarding inference algorithms for hierarchical Bayesian networks. More specifically we focus on a specific type of networks which result in highly sparse models for the input. Bayesian inference in these networks usually is based on optimising a non-convex cost function of the model parameters. We extend previous work done in this field by providing...
A bird phrase segmentation method using entropy-based change point detection is proposed. Spectrograms of bird calls are usually sparse while the background noise is relatively white. Therefore, considering the entropy of a sliding time-frequency block on the spectrogram, the entropy dips when detecting a signal and rises when the signal ends. Rather than applying a hard threshold on the entropy to...
We describe a Bayesian learning scheme for the hierarchal Bayesian linear model, which is based on the Gaussian scale mixture (GSM) modeling of the distribution of the latent variable. The proposed method takes advantage of the hierarchal Gaussian structure for a simple Monte-Carlo sampling algorithm. Particularly, with a single hidden scale parameter controlling the distribution of the latent variables,...
Latent feature models (LFMs) have been widely used to model ordinal rating data and relational network data in various tasks such as collaborative filtering and link prediction, typically in a generative way. Alternatively, one might incorporate max-margin learning into the model via the principle of Maximum Entropy Discrimination (MED) to learn a more discriminative latent feature space that favors...
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