sCOs: Semi-supervised co-selection by a similarity preserving approach

2021 ◽  
pp. 1-1
Author(s):  
Khalid Benabdeslem ◽  
Dou El Kefel Mansouri ◽  
Raywat Makkhongkaew
Keyword(s):  
Similarity Preserving

Spectral similarity-preserving hyperspectral band selection

2013 ◽  
Vol 4 (10) ◽  
pp. 969-978 ◽  
Author(s):  
Shijin Li ◽  
Yuelong Zhu ◽  
Dingsheng Wan ◽  
Jun Feng
Keyword(s):  
Band Selection ◽  
Spectral Similarity ◽  
Similarity Preserving

Multi‐bit quantisation for similarity‐preserving hashing

2018 ◽  
Vol 12 (5) ◽  
pp. 616-622
Author(s):  
Liang Liang Su ◽  
Jun Tang ◽  
Dong Liang ◽  
Ming Zhu
Keyword(s):  
Similarity Preserving

Mass transfer into boundary layers for power law fluids

1979 ◽  
Vol 365 (1722) ◽  
pp. 313-326 ◽  
Keyword(s):  
Boundary Layer ◽  
Mass Transfer ◽  
Shear Stress ◽  
Boundary Value Problem ◽  
Wall Friction ◽  
Point Boundary ◽  
Boundary Layer Equations ◽  
Mass Injection ◽  
Power Law Fluids ◽  
Similarity Preserving

The boundary layer equations for the class of non-Newtonian fluids having the shear stress proportional to a power of the strain rate are considered under conditions of similarity-preserving mass transfer at the wall. The adoption of Crocco variables results in a nonlinear, two point boundary value problem for which existence, uniqueness and analyticity are established. In the case of mass injection particular attention is paid to boundary conditions corresponding to the vanishing of the wall friction and values for the (possibly non-existent) critical injection rates are exhibited.

Involution similarity preserving linear maps

2019 ◽  
Vol 249 (3) ◽  
pp. 319-328 ◽  
Author(s):  
Zijie Qin ◽  
Fangyan Lu
Keyword(s):  
Linear Maps ◽  
Similarity Preserving

SeqPAM

2008 ◽  
pp. 17-38 ◽  
Author(s):  
Pradeep Kumar Kumar ◽  
Raju S. Bapi ◽  
P. Radha Krishna
Keyword(s):  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Sequential Data ◽  
Cluster Validation ◽  
Web Personalization ◽  
Similarity Preserving ◽  
Validation Technique ◽  
The Web

With the growth in the number of web users and necessity for making information available on the web, the problem of web personalization has become very critical and popular. Developers are trying to customize a web site to the needs of specific users with the help of knowledge acquired from user navigational behavior. Since user page visits are intrinsically sequential in nature, efficient clustering algorithms for sequential data are needed. In this paper, we introduce a similarity preserving function called sequence and set similarity measure S3M that captures both the order of occurrence of page visits as well as the content of pages. We conducted pilot experiments comparing the results of PAM, a standard clustering algorithm, with two similarity measures: Cosine and S3M. The goodness of the clusters resulting from both the measures was computed using a cluster validation technique based on average levensthein distance. Results on pilot dataset established the effectiveness of S3M for sequential data. Based on these results, we proposed a new clustering algorithm, SeqPAM for clustering sequential data. We tested the new algorithm on two datasets namely, cti and msnbc datasets. We provided recommendations for web personalization based on the clusters obtained from SeqPAM for msnbc dataset.

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