18,661 research outputs found
Consensus clustering approach to group brain connectivity matrices
A novel approach rooted on the notion of consensus clustering, a strategy
developed for community detection in complex networks, is proposed to cope with
the heterogeneity that characterizes connectivity matrices in health and
disease. The method can be summarized as follows:
(i) define, for each node, a distance matrix for the set of subjects by
comparing the connectivity pattern of that node in all pairs of subjects; (ii)
cluster the distance matrix for each node; (iii) build the consensus network
from the corresponding partitions; (iv) extract groups of subjects by finding
the communities of the consensus network thus obtained.
Differently from the previous implementations of consensus clustering, we
thus propose to use the consensus strategy to combine the information arising
from the connectivity patterns of each node. The proposed approach may be seen
either as an exploratory technique or as an unsupervised pre-training step to
help the subsequent construction of a supervised classifier. Applications on a
toy model and two real data sets, show the effectiveness of the proposed
methodology, which represents heterogeneity of a set of subjects in terms of a
weighted network, the consensus matrix
Optimizing Ranking Measures for Compact Binary Code Learning
Hashing has proven a valuable tool for large-scale information retrieval.
Despite much success, existing hashing methods optimize over simple objectives
such as the reconstruction error or graph Laplacian related loss functions,
instead of the performance evaluation criteria of interest---multivariate
performance measures such as the AUC and NDCG. Here we present a general
framework (termed StructHash) that allows one to directly optimize multivariate
performance measures. The resulting optimization problem can involve
exponentially or infinitely many variables and constraints, which is more
challenging than standard structured output learning. To solve the StructHash
optimization problem, we use a combination of column generation and
cutting-plane techniques. We demonstrate the generality of StructHash by
applying it to ranking prediction and image retrieval, and show that it
outperforms a few state-of-the-art hashing methods.Comment: Appearing in Proc. European Conference on Computer Vision 201
HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Recently, crowdsourcing has emerged as an effective paradigm for
human-powered large scale problem solving in various domains. However, task
requester usually has a limited amount of budget, thus it is desirable to have
a policy to wisely allocate the budget to achieve better quality. In this
paper, we study the principle of information maximization for active sampling
strategies in the framework of HodgeRank, an approach based on Hodge
Decomposition of pairwise ranking data with multiple workers. The principle
exhibits two scenarios of active sampling: Fisher information maximization that
leads to unsupervised sampling based on a sequential maximization of graph
algebraic connectivity without considering labels; and Bayesian information
maximization that selects samples with the largest information gain from prior
to posterior, which gives a supervised sampling involving the labels collected.
Experiments show that the proposed methods boost the sampling efficiency as
compared to traditional sampling schemes and are thus valuable to practical
crowdsourcing experiments.Comment: Accepted by AAAI201
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