Topic
Mahalanobis distance
About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.
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TL;DR: BoostMetric as mentioned in this paper uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process to learn a Mahalanobis distance metric.
Abstract: The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed \BoostMetric, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used to enforce this constraint, but does not scale well. \BoostMetric is instead based on a key observation that any positive semidefinite matrix can be decomposed into a linear positive combination of trace-one rank-one matrices. \BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting method is easy to implement, does not require tuning, and can accommodate various types of constraints. Experiments on various datasets show that the proposed algorithm compares favorably to those state-of-the-art methods in terms of classification accuracy and running time.
87 citations
TL;DR: In this paper, an influence weight is assigned to each predictor in the conditioning set with the aim of identifying nearest neighbours that represent the conditional dependence in an improved manner, and the workability of the proposed modification is tested using synthetic data from known linear and nonlinear models and its applicability is illustrated through an example where daily rainfall is downscaled over 15 stations near Sydney, Australia using a predictor set consisting of selected large-scale atmospheric circulation variables.
87 citations
TL;DR: Three methods for the identification of multivariate outliers are compared based on the Mahalanobis distance that will be made resistant against outliers and model deviations by robust estimation of location and covariance.
Abstract: Three methods for the identification of multivariate outliers (Rousseeuw and Van Zomeren, 1990; Becker and Gather, 1999; Filzmoser et al, 2005) are compared They are based on the Mahalanobis distance that will be made resistant against outliers and model deviations by robust estimation of location and covariance The comparison is made by means of a simulation study Not only the case of multivariate normally distributed data, but also heavy tailed and asymmetric distributions will be considered The simulations are focused on low dimensional ( p = 5 ) and high dimensional ( p = 30 ) data
87 citations
11 Jun 2014
TL;DR: A generalization of the SHADE protocol, called GSHADE, that enables privacy-preserving computation of several distance metrics, including (normalized) Hamming distance, Euclidean distance, Mahalanobis distance, and scalar product.
Abstract: At WAHC'13, Bringer et al. introduced a protocol called SHADE for secure and efficient Hamming distance computation using oblivious transfer only. In this paper, we introduce a generalization of the SHADE protocol, called GSHADE, that enables privacy-preserving computation of several distance metrics, including (normalized) Hamming distance, Euclidean distance, Mahalanobis distance, and scalar product. GSHADE can be used to efficiently compute one-to-many biometric identification for several traits (iris, face, fingerprint) and benefits from recent optimizations of oblivious transfer extensions. GSHADE allows identification against a database of 1000 Eigenfaces in 1.28 seconds and against a database of 10000 IrisCodes in 17.2 seconds which is more than 10 times faster than previous works.
86 citations
24 Oct 2005
TL;DR: This paper presents a region-based algorithm for accurate license plate localization, where mean shift is utilized to filter and segment color vehicle images into candidate regions and the Mahalanobis classifier is used to classify license plate regions and non-license plate regions.
Abstract: This paper presents a region-based algorithm for accurate license plate localization, where mean shift is utilized to filter and segment color vehicle images into candidate regions Three features are extracted in order to decide whether a candidate region represents a real license plate, namely, rectangularity, aspect ratio, and edge density Then, the Mahalanobis classifier is used with respect to above three features to classify license plate regions and non-license plate regions Experimental results show that the proposed algorithm produces high robustness and accuracy
85 citations