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書目名稱Graph Embedding for Pattern Analysis影響因子(影響力)




書目名稱Graph Embedding for Pattern Analysis影響因子(影響力)學(xué)科排名




書目名稱Graph Embedding for Pattern Analysis網(wǎng)絡(luò)公開度




書目名稱Graph Embedding for Pattern Analysis網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Graph Embedding for Pattern Analysis被引頻次




書目名稱Graph Embedding for Pattern Analysis被引頻次學(xué)科排名




書目名稱Graph Embedding for Pattern Analysis年度引用




書目名稱Graph Embedding for Pattern Analysis年度引用學(xué)科排名




書目名稱Graph Embedding for Pattern Analysis讀者反饋




書目名稱Graph Embedding for Pattern Analysis讀者反饋學(xué)科排名

委派

Migration, Bildung und SpracherwerbLLE uses linear coefficients, which reconstruct a given example by its neighbors, to represent the local geometry, and then seeks a low-dimensional embedding, in which these coefficients are still suitable for reconstruction. ISOMAP preserves global geodesic distances of all the pairs of examples.

強(qiáng)壯

https://doi.org/10.1007/978-3-476-04372-6ed algorithm, aims to maximize the inter-class scatter and at the same time minimize the intra-class scatter. Due to utilization of label information, LDA is experimentally reported to outperform PCA for face recognition, when sufficient labeled face images are provided [2].

盡責(zé)

bizarre

Feature Grouping and Selection Over an Undirected Graph,atures lasso tends to only select one of those features resulting in suboptimal performance [25]. Several methods have been proposed to address this issue in the literature. Shen and Ye [15] introduce an adaptive model selection procedure that corrects the estimation bias through a data-driven penalty based on generalized degrees of freedom.

緊張過(guò)度

緊張過(guò)度

A Flexible and Effective Linearization Method for Subspace Learning,ed algorithm, aims to maximize the inter-class scatter and at the same time minimize the intra-class scatter. Due to utilization of label information, LDA is experimentally reported to outperform PCA for face recognition, when sufficient labeled face images are provided [2].

Cougar

Graph Embedding for Speaker Recognition,sures speaker similarity. Using speaker comparison, other applications can be implemented—speaker clustering (grouping similar speakers in a corpus), speaker verification (verifying a claim of identity), speaker identification (identifying a speaker out of a list of potential candidates), and speaker retrieval (finding matches to a query set).

modish

https://doi.org/10.1007/978-3-658-08301-4ble to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant.

脫水