Convex Sparsification of Unstructured Aggregated Data


Convex Sparsification of Unstructured Aggregated Data – The problem of segmentation of unstructured data, also known as unstructured sparse coding, involves solving a sparse coding problem which encodes a sequence of unstructured variables into a sparse coding block. Unstructured coding (NC) offers a fast solution for sparse coding problems with a fixed representation. In this paper, the representation of the input data is represented by a matrix, which is considered as a sparse coding matrix. In order to solve the sparse coding problem, we propose an efficient formulation of NC, which is based on a non-convex optimization problem. An algorithm for solving the NC problem is presented. Experiments on a variety of datasets show a significant reduction in the size and computation time (up to 10^10 m imes 10^8) compared with the classical NC, which uses data from multiple viewpoints and which requires to maintain a constant size matrix dimension. By minimizing the dimension of the matrix, the proposed algorithm is also able to obtain high accuracy results without significant computation overhead.

This paper presents a Bayesian inference framework for Bayesian regression on graphs based on a Bayesian kernel density model. The main contribution of this paper is a new Bayesian estimation method for hierarchical clustering in graphs. The Bayesian kernel density model is derived from a logistic regression algorithm that is based on the spectral clustering of node segments. This method is compared to several similar methods for hierarchical clustering in graphs. The results show that the proposed method provides a better performance than these two approaches while having a lower computational cost.

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Convex Sparsification of Unstructured Aggregated Data

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    Discovery of Nonlinear Structural Relations with Hierarchical Feature PriorsThis paper presents a Bayesian inference framework for Bayesian regression on graphs based on a Bayesian kernel density model. The main contribution of this paper is a new Bayesian estimation method for hierarchical clustering in graphs. The Bayesian kernel density model is derived from a logistic regression algorithm that is based on the spectral clustering of node segments. This method is compared to several similar methods for hierarchical clustering in graphs. The results show that the proposed method provides a better performance than these two approaches while having a lower computational cost.


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