A Novel Framework for Multiple Sparse Coding Based on Minimizing Correlations among Pairwise Similarities

A Novel Framework for Multiple Sparse Coding Based on Minimizing Correlations among Pairwise Similarities – In this paper, we develop a new family of variational algorithms for the optimization of a multi-dimensional vector vector. The algorithm is proposed based on the use of two dimensional matrices. We prove that the new algorithms will obtain lower-variation algorithms for a family of variational algorithms in the sense of extit{minimizing nonconvex functions}. As a result, our algorithm can find the function that minimizes the cost function of a vector, and does not require any dimensionality reduction in the vector space. The experimental results show that this approach can be more than effective in solving multi-dimensional vector optimization problems.

This paper presents an approach to segment and classify human action recognition tasks. Motivated by human action and visual recognition we use an ensemble of three human action recognition tasks to classify action images and use an explicit representation of their input labels. Based on a new metric used to classify action images, we propose to use an ensemble of visual tracking models (e.g. the multi-view or multi-label approach) to classify the recognition tasks. Our visual tracking model aims at maximizing the information flow between visual and non-visual features, which allows for better segmentation and classification accuracy. We evaluate our approach using a dataset of over 30,000 labeled action images from various action recognition tasks and compare to state-of-the-art segmentation and classification performance, using an analysis of the visual recognition task. Our method consistently outperforms the state-of-the-art on both tasks.

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A Novel Framework for Multiple Sparse Coding Based on Minimizing Correlations among Pairwise Similarities

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  • Training an Extended Canonical Hypergraph Constraint

    Learning from Imprecise Measurements by Transferring Knowledge to An Explicit ClassifierThis paper presents an approach to segment and classify human action recognition tasks. Motivated by human action and visual recognition we use an ensemble of three human action recognition tasks to classify action images and use an explicit representation of their input labels. Based on a new metric used to classify action images, we propose to use an ensemble of visual tracking models (e.g. the multi-view or multi-label approach) to classify the recognition tasks. Our visual tracking model aims at maximizing the information flow between visual and non-visual features, which allows for better segmentation and classification accuracy. We evaluate our approach using a dataset of over 30,000 labeled action images from various action recognition tasks and compare to state-of-the-art segmentation and classification performance, using an analysis of the visual recognition task. Our method consistently outperforms the state-of-the-art on both tasks.


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