منابع مشابه
Projection Based M-Estimators
Random Sample Consensus (RANSAC) is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use for practical applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important difficulty still remains. The algorithm is not user independent, and requi...
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A new robust clustering algorithm, called generalized annealing M-estimator (GAM-estimator), is proposed. Initialized with multiple seeds, the GAM-estimator converges to several optimal cluster centers. Neither knowledge about the number of clusters nor scale is needed. The global optimal solution of clustering is achieved by minimization of an objective function. The algorithm is applied to un...
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The robust regression techniques in the RANSAC family are popular today in computer vision, but their performance depends on a user supplied threshold. We eliminate this drawback of RANSAC by reformulating another robust method, the M-estimator, as a projection pursuit optimization problem. The projection based pbM-estimator automatically derives the threshold from univariate kernel density est...
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We consider the problem of estimating the stationary density of the process Vt in the stochastic volatility model dYt = √ VtdWt whereWt is a standard Brownian motion and Vt a Markov stationary mixing process. We propose a nonparametric adaptive strategy for which we give non asymptotic risk bounds. We discuss the resulting rate and show that it is quite good in some classical examples of volati...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2012
ISSN: 0162-8828,2160-9292
DOI: 10.1109/tpami.2012.52