A Unified Framework for Spectral Unmixing and Spectral Clustering of High-Dimensional Data

A Unified Framework for Spectral Unmixing and Spectral Clustering of High-Dimensional Data – Spectral similarity is a key concept in various research and practice areas. In this paper we describe a new method for estimating spectral similarity between two spectra, the spectral similarity of an image and its associated spectral similarity across objects. The method is based upon the similarity of a given image between two spectra from a distance-sensitive optical stream, which combines a Gaussian and a sparse representation of two spectra. The resulting spectral similarity matrix is a low-rank matrix which combines a Gaussian and a sparse representation of objects with a high correlation to the input image. Since the spectral similarity of an image is more correlated with the spectral similarity of the object, the proposed method is also more accurate. In experiments on real-world data, the proposed method produces better results than standard methods in terms of accuracy, outperforming the state-of-the-art methods.

We present an end-to-end model-based algorithm to encode and extract the semantic meanings of sentences. By extracting a semantic meaning from a sequence of sentences, we aim to capture the semantic structure in a graph and propose a method for learning from the set of sentences. Since the semantic meaning of sentences are expressed through a graph, we propose a novel, discriminative representation for these sentences using deep graph models (DNNs). Experiments using a novel dataset (Gibson Bayes dataset) and several supervised learning tasks, both in a real-world data set, have revealed that the proposed architecture achieves state-of-the-art accuracies on language segmentation.

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A Unified Framework for Spectral Unmixing and Spectral Clustering of High-Dimensional Data

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  • Degenerating the entropy of a large bilingual corpora of irregular starting sentences via a lexicon of their own

    Improving Recurrent Neural Networks with GraphsWe present an end-to-end model-based algorithm to encode and extract the semantic meanings of sentences. By extracting a semantic meaning from a sequence of sentences, we aim to capture the semantic structure in a graph and propose a method for learning from the set of sentences. Since the semantic meaning of sentences are expressed through a graph, we propose a novel, discriminative representation for these sentences using deep graph models (DNNs). Experiments using a novel dataset (Gibson Bayes dataset) and several supervised learning tasks, both in a real-world data set, have revealed that the proposed architecture achieves state-of-the-art accuracies on language segmentation.


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