Jianguang Zhang

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The effectiveness of supervised feature selection degrades in low training data scenarios. We propose to alleviate this problem by augmenting per-task feature selection with joint feature selection over multiple tasks. Our algorithm builds on the assumption that different tasks have shared structure which could be utilized to cope with data sparsity. The(More)
Non-invasive prenatal testing (NIPT) is currently used as a frontline screening test to identify fetuses with common aneuploidies. Occasionally, incidental NIPT results are conveyed to the clinician suggestive of fetuses with rare chromosome disease syndromes. We describe a child with trisomy 9 (T9) mosaicism where the prenatal history reported a positive(More)
The spatial information is the important cue for human action recognition. Different from the vector representation, the spatial structure of human action in the still images can be preserved by the tensor representation. This paper proposes a robust human action recognition algorithm by tensor representation and Tucker decomposition. In this method, the(More)
In this paper, we propose a new tensor-based representation algorithm for image classification. The algorithm is realized by learning the parameter tensor for image tensors. One novelty is that the parameter tensor is learned according to the Tucker tensor decomposition as the multiplication of a core tensor with a group of matrices for each order, which(More)
Human action recognition from motion videos plays an important role in multimedia analysis. Different from the temporal cues of action series in motion videos, the motion tendency can also be revealed from the still images or key frames. Thus, if the action knowledge in related still images can be well adapted to the target motion videos, we would have a(More)
Feature selection is an important step for large-scale image data analysis, which has been proved to be difficult due to large size in both dimensions and samples. Feature selection firstly eliminates redundant and irrelevant features and then chooses a subset of features that performs as efficient as the complete set. Generally, supervised feature(More)
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