Automatically finding clusters in normalized cuts
نویسندگان
چکیده
منابع مشابه
Automatically finding clusters in normalized cuts
Normalized Cuts is a state-of-the-art spectral method for clustering. By applying spectral techniques, the data becomes easier to cluster and then k-means is classically used. Unfortunately the number of clusters must be manually set and it is very sensitive to initialization. Moreover, k-means tends to split large clusters, to merge small clusters, and to favor convex-shaped clusters. In this ...
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Vector space representations of words capture many aspects of word similarity, but such methods tend to make vector spaces in which antonyms (as well as synonyms) are close to each other. We present a new signed spectral normalized graph cut algorithm, signed clustering, that overlays existing thesauri upon distributionally derived vector representations of words, so that antonym relationships ...
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We propose a novel approach for solving the perceptual grouping problem in vision. Rather than fo-cusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmenta-tion as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut ...
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w e propose Q novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the amage data, our approach aims a t extracting the global impression of an image. We treat image segmentation QS (I graph partitioning problem and propose Q novel global criterion, the normalized cut, for segmenting the graph. The normalized cut...
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2011
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2011.01.003