Yael Yankelevsky

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In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the observations. A second graph regularization gives similar treatment to the feature domain and helps in learning a more(More)
Dictionary learning (DL) techniques aim to find sparse signal representations that capture prominent characteristics in a given data. Such methods operate on a data matrix Y ∈ RN×M, where each of its columns yi ∈ RN constitutes a training sample, and these columns together represent a sampling from the data manifold. For signals y(More)
Sonography techniques use multiple transducer elements for tissue visualization. The signals detected at each element are combined in the process of digital beamforming, requiring that large amounts of data be acquired, transferred and processed. One of the main challenges is reducing the data size while retaining the image contents. For this purpose, we(More)
In this work, we tackle the problem of multilabel classification using a sparsity-based approach. Multi-label classification problems, in which each instance is associated with a set of multiple labels, have received significant attention over the past few years due to the ongoing growth of data dimensions and availability. However, the dependency between(More)
Component-based modeling and processing can be found in many signal and image processing applications. In this paper, we apply this approach to cardiac medical ultrasound imaging. We show how the raw data generated in the ultrasound scanning process can be modeled as consisting of two distinct components. By decomposing these signals into the two(More)
An autostereogram is a single image that encodes depth information that pops out when looking at it. The trick is achieved by setting a basic 2D pattern and continuously replicating the local pattern at each point in the image with a shift defined by the desired disparity. In this work, we explore the dependency between the ease of perceiving depth in(More)
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