Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification

Peng, Jing; Heisterkamp, Douglas R.; Dai, Honghua

doi:10.1007/10984697_8

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…Many extensions of the basic method have been proposed over the years. It is possible to use kNN in conjunction with kernels [3], perform large margin learning [4], multi-label classification [5], adaptively determine the neighborhood size [6], etc.…”

Section: Introductionmentioning

confidence: 99%

Hub Co-occurrence Modeling for Robust High-Dimensional kNN Classification

Tomašev

Mladenić

2013

Advanced Information Systems Engineering

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Abstract. The emergence of hubs in k-nearest neighbor (kNN) topologies of intrinsically high dimensional data has recently been shown to be quite detrimental to many standard machine learning tasks, including classification. Robust hubness-aware learning methods are required in order to overcome the impact of the highly uneven distribution of influence. In this paper, we have adapted the Hidden Naive Bayes (HNB) model to the problem of modeling neighbor occurrences and co-occurrences in high-dimensional data. Hidden nodes are used to aggregate all pairwise occurrence dependencies. The result is a novel kNN classification method tailored specifically for intrinsically high-dimensional data, the Augmented Naive Hubness Bayesian k nearest Neighbor (ANHBNN). Neighbor co-occurrence information forms an important part of the model and our analysis reveals some surprising results regarding the influence of hubness on the shape of the co-occurrence distribution in high-dimensional data. The proposed approach was tested in the context of object recognition from images in class imbalanced data and the results show that it offers clear benefits when compared to the other hubness-aware kNN baselines.

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Section: Introductionmentioning

confidence: 99%

Hub Co-occurrence Modeling for Robust High-Dimensional kNN Classification

Tomašev

Mladenić

2013

Advanced Information Systems Engineering

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“…For instance, (i) its performance highly depends on the selection of k; (ii) pooling nearest neighbors from training data that contain overlapping classes is considered unsuitable; (iii) the so-called curse of dimensionality can severely hurt its performance in finite samples [25,26]; and finally (iv) the selection of the distance metric is crucial to determine the outcome of the nearest neighbor classification [26].…”

Section: Introductionmentioning

confidence: 99%

Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery

Samaniego

Schulz

2009

Remote Sensing

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Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML) or k-Nearest Neighbor (k-NN) indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.

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“…Prior work in adaptive neighborhoods for k-NN has largely focused on locally adjusting the distance metric [11]- [20]. The rationale behind these adaptive metrics is that many feature spaces are not isotropic and the discriminability provided by each feature dimension is not constant throughout the space.…”

Section: Enclosing Neighborhoodsmentioning

confidence: 99%

Adaptive Local Linear Regression with Application to Printer Color Management

Gupta¹,

Garcia²,

Chin³

2008

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Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross-validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross-validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating look-up tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples.

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Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification

Cited by 6 publications

References 18 publications

Hub Co-occurrence Modeling for Robust High-Dimensional kNN Classification

Hub Co-occurrence Modeling for Robust High-Dimensional kNN Classification

Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery

Adaptive Local Linear Regression with Application to Printer Color Management

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