Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
156
Social sparsity! neighborhood systems enrich structured shrinkage operators
- IEEE Trans. Signal Processing
, 2013
"... Abstract—Sparse and structured signal expansions on dictionaries can be obtained through explicit modeling in the coefficient domain. The originality of the present article lies in the construction and the study of generalized shrinkage operators, whose goal is to identify structured significance ma ..."
Abstract
-
Cited by 8 (2 self)
- Add to MetaCart
maps and give rise to structured thresholding. These generalize Group Lasso and the previously introduced Elitist Lasso by introducing more flexibility in the coefficient domain modeling, and lead to the notion of social sparsity. The proposed operators are studied theoretically and embedded
On Model Selection Consistency of Lasso
, 2006
"... Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such models, but usually involves a computationally heavy combinatorial search. Lasso (Tibshirani, 1996) is now being used ..."
Abstract
-
Cited by 477 (20 self)
- Add to MetaCart
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such models, but usually involves a computationally heavy combinatorial search. Lasso (Tibshirani, 1996) is now being
Reliable Social Trust Management with Mitigating Sparsity Problem
"... Abstract Social networks express the information flows of individuals or groups using actors and relationships. Along with the growth of WWW, interest in large-scaled social networks has grown bigger. However, social networks being applied in diverse areas are facing with sparsity problems resultin ..."
Abstract
- Add to MetaCart
Abstract Social networks express the information flows of individuals or groups using actors and relationships. Along with the growth of WWW, interest in large-scaled social networks has grown bigger. However, social networks being applied in diverse areas are facing with sparsity problems
Countering sparsity and vulnerabilities in reputation systems
, 2005
"... While web applications provide enormous opportunities, they also present potential threats and risks due to a lack of trust among users. Reputation systems provide a promising way for building trust through social control by harnessing the community feedback in the form of feedback. However, reputat ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
While web applications provide enormous opportunities, they also present potential threats and risks due to a lack of trust among users. Reputation systems provide a promising way for building trust through social control by harnessing the community feedback in the form of feedback. However
Addressing the Sparsity of Location Information on Twitter
"... Micro-blogging services such as Twitter have gained enormous popularity over the last few years leading to massive volumes of user generated content. In combination with the proliferation of smart-phones, this information is generated live from a multitude of content contributors. Interestingly, the ..."
Abstract
- Add to MetaCart
, information regarding location is very rare since many users do not accurately specify their location, and fewer posts have geographic coordinates. In this work, we aim to confront this data sparsity issue. Utilizing Twitter’s social graph and content, we are able to obtain users from a specific location. We
Robust PCA as bilinear decomposition with outlier-sparsity regularization
- IEEE TRANS. SIGNAL PROCESS
, 2012
"... Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics. In this context, the fresh look advocated here permeates benefit ..."
Abstract
-
Cited by 16 (3 self)
- Add to MetaCart
benefits from variable selection and compressive sampling, to robustify PCA against outliers. A least-trimmed squares estimator of a low-rank bilinear factor analysis model is shown closely related to that obtained from an-(pseudo)norm-regularized criterion encouraging sparsity in a matrix explicitly
Countering Feedback Sparsity and Manipulation in Reputation Systems
"... Abstract—Reputation systems provide a promising way for building trust through social control in collaborative communities by harnessing the community knowledge in the form of feedback. However, reputation systems also introduce vulnerabilities due to potential manipulations by dishonest or maliciou ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
Abstract—Reputation systems provide a promising way for building trust through social control in collaborative communities by harnessing the community knowledge in the form of feedback. However, reputation systems also introduce vulnerabilities due to potential manipulations by dishonest
Social Trust as a solution to address sparsity-inherent problems of Recommender systems
"... Trust has been explored by many researchers in the past as a successful solution for assisting recommender systems. Even though the approach of using a web-of-trust scheme for assisting the recommendation production is well adopted, issues like the sparsity problem have not been explored adequately ..."
Abstract
-
Cited by 5 (0 self)
- Add to MetaCart
Trust has been explored by many researchers in the past as a successful solution for assisting recommender systems. Even though the approach of using a web-of-trust scheme for assisting the recommendation production is well adopted, issues like the sparsity problem have not been explored adequately
SPARSITY-COGNIZANT OVERLAPPING CO-CLUSTERING FOR BEHAVIOR INFERENCE IN SOCIAL NETWORKS
"... Co-clustering can be viewed as a two-way (bilinear) factorization of a large data matrix into dense/uniform and possibly overlapping submatrix factors (co-clusters). This combinatorially complex problem emerges in several applications, including behavior inference tasks encountered with social netwo ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
Co-clustering can be viewed as a two-way (bilinear) factorization of a large data matrix into dense/uniform and possibly overlapping submatrix factors (co-clusters). This combinatorially complex problem emerges in several applications, including behavior inference tasks encountered with social
SPARSITY-COGNIZANT OVERLAPPING CO-CLUSTERING FOR BEHAVIOR INFERENCE IN SOCIAL NETWORKS
"... Co-clustering can be viewed as a two-way (bilinear) factorization of a large data matrix into dense/uniform and possibly overlapping sub-matrix factors (co-clusters). This combinatorially complex problem emerges in several applications, including behavior inference tasks encountered with social netw ..."
Abstract
- Add to MetaCart
Co-clustering can be viewed as a two-way (bilinear) factorization of a large data matrix into dense/uniform and possibly overlapping sub-matrix factors (co-clusters). This combinatorially complex problem emerges in several applications, including behavior inference tasks encountered with social
Results 1 - 10
of
156
