Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText.
Kick-your-searchterm to multiple Engines kick-your-query now !>
A dictionary by aggregated review articles of nephrology, medicine and the life sciences
Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

suck abstract from ncbi


suck pdf from google scholar
unlimited free pdf from europmcC7677445    free
PDF from PMC    free
html from PMC    free

suck abstract from ncbi

Nephropedia Template TP Text

Twit Text FOAVip

Twit Text #

English Wikipedia

  • Cross-covariance based affinity for graphs #MMPMIDC7677445
  • Yadav RK; Abhishek; Verma S; Venkatesan S
  • ä-/-ä ä[]; ä (ä): 1-21 PMIDC7677445show ga
  • The accuracy of graph based learning techniques relies on the underlying topological structure and affinity between data points, which are assumed to lie on a smooth Riemannian manifold. However, the assumption of local linearity in a neighborhood does not always hold true. Hence, the Euclidean distance based affinity that determines the graph edges may fail to represent the true connectivity strength between data points. Moreover, the affinity between data points is influenced by the distribution of the data around them and must be considered in the affinity measure. In this paper, we propose two techniques, CCGAL and CCGAN that use cross-covariance based graph affinity (CCGA) to represent the relation between data points in a local region. CCGAL also explores the additional connectivity between data points which share a common local neighborhood. CCGAN considers the influence of respective neighborhoods of the two immediately connected data points, which further enhance the affinity measure. Experimental results of manifold learning on synthetic datasets show that CCGA is able to represent the affinity measure between data points more accurately. This results in better low dimensional representation. Manifold regularization experiments on standard image dataset further indicate that the proposed CCGA based affinity is able to accurately identify and include the influence of the data points and its common neighborhood that increase the classification accuracy. The proposed method outperforms the existing state-of-the-art manifold regularization methods by a significant margin.
  • ä

  • DeepDyve
  • Pubget Overpricing
  • suck abstract from ncbi

    1 ä.ä ä