Sparse and Robust Principal Component Analysis


Sparse and Robust Principal Component Analysis – We propose a novel method to jointly discover the features of a mixture of a sparse and a Robust classifier. We use a variant of the classic CNN+CNN-Mixture framework called a mixture-of-a-box-and-the-layers (MD-CNN) to learn a representation for these features. We also generalize this framework to consider a variety of complex data types. We present a new dataset, and a preliminary analysis on our MD-CNN, and demonstrate its advantages, including greater performance on classification instances than on standard datasets.

We develop a new algorithm for the task of detection of human joints in 3D images. The proposed method consists of two stages, detecting human joints in 3D images and comparing their characteristics over all possible combinations. A joint is classified as having three or more attributes: a solidified shape, a structure (dura) and an affine surface. For a complete classification process of joints, we define joints based on the shapes and affine surfaces. We also propose a novel framework for the classification of joints and identify the relevant joints. The proposed method can be viewed as a method for joint labeling and its implementation can be used in different 3D applications.

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Sparse and Robust Principal Component Analysis

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  • Nonlinear regression and its application to path inference: the LIFE case

    Neural network classification based on membrane lesion detection and lesion structure selectionWe develop a new algorithm for the task of detection of human joints in 3D images. The proposed method consists of two stages, detecting human joints in 3D images and comparing their characteristics over all possible combinations. A joint is classified as having three or more attributes: a solidified shape, a structure (dura) and an affine surface. For a complete classification process of joints, we define joints based on the shapes and affine surfaces. We also propose a novel framework for the classification of joints and identify the relevant joints. The proposed method can be viewed as a method for joint labeling and its implementation can be used in different 3D applications.


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