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Adaptive quasiconformal kernel nearest neighbor classification

Jing Peng, D.R. Heisterkamp, H.K. Dai
2004 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose an adaptive nearest neighbor classification method to try to minimize bias.  ...  We use quasiconformal transformed kernels to compute neighborhoods over which the class probabilities tend to be more homogeneous. As a result, better classification performance can be expected.  ...  Fig. 1 summarizes our proposed adaptive quasiconformal kernel nearest neighbor classification algorithm.  ... 
doi:10.1109/tpami.2004.1273978 pmid:15460287 fatcat:5k4ln7nbfffhhkyeeg5iser6se

LDA/SVM driven nearest neighbor classification

Jing Peng, D.R. Heisterkamp, H.K. Dai
2003 IEEE Transactions on Neural Networks  
The nearest neighbor rule introduces severe bias under these conditions. We propose a locally adaptive neighborhood morphing classification method to try to minimize bias.  ...  Nearest neighbor classification relies on the assumption that class conditional probabilities are locally constant.  ...  This paper presents an adaptive metric method for effective pattern classification.  ... 
doi:10.1109/tnn.2003.813835 pmid:18238072 fatcat:5c5fgw3idrdn5e7flbl6wio5lq

Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval

Jing Liu, Yulong Qiao
2020 Wireless Communications and Mobile Computing  
The contributions include three points: the quasiconformal mapping-based multiple kernel learning network framework is proposed for hyperspectral data classification, the Mahalanobis distance kernel function  ...  In this paper, we present a quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification algorithm for internet-based hyperspectral data retrieval.  ...  Two popular criterions, Fisher criterion and large margin nearest neighbor criterion, are used in similar-/dissimilar-based learning. Figure 2 : 2 Data preprocessing procedure.  ... 
doi:10.1155/2020/8873366 fatcat:ncc27czp6vdffbq5lypirw5fju

Kernel Vector Approximation Files for Relevance Feedback Retrieval in Large Image Databases

Douglas R. Heisterkamp, Jing Peng
2005 Multimedia tools and applications  
This paper introduces a novel KVA-File (kernel VA-File) that extends VA-File to kernel-based retrieval methods.  ...  Thus an effective indexing method is provided for kernel-based relevance feedback image retrieval methods.  ...  Adaptive Quasiconformal Kernel Adaptive quasiconformal kernel (AQK) distance [9, 16] combines the kernel distance (1) with a quasiconformal mapping [1] , k (a, b) = c(a)c(b)k (a, b) , (2) to create  ... 
doi:10.1007/s11042-005-0454-4 fatcat:jy3vud6i5fgcdccp4b3cepfwee

A Semi-Supervised Metric Learning for Content-Based Image Retrieval

I. Daoudi, K. Idrissi
2011 International Journal of Computer Vision and Image Processing  
Index Terms-Similarity search, kernel functions, CBIR, k nearest neighbor search  ...  Experimental results show that our kernel-based approach not only improves the retrieval performances of kernel distance without learning, but also outperforms other kernel metric learning methods.  ...  Particularly, in [5] , a Quasiconformal Kernel for nearest neighbor classification is proposed which adjusts the Radial Basis function by introducing weights based on both local consistency of class labels  ... 
doi:10.4018/ijcvip.2011070104 fatcat:mcdplhdfcfcftpcrrqlqgr4bje

Manifold Based Local Classifiers: Linear and Nonlinear Approaches

Hakan Cevikalp, Diane Larlus, Marian Neamtu, Bill Triggs, Frederic Jurie
2008 Journal of Signal Processing Systems  
When such regions lie close to inter-class boundaries, the nearest neighbors of a query may lie in the wrong class, thus leading to errors in the Nearest Neighbor classification rule.  ...  We then extend both methods to the nonlinear case by mapping the nearest neighbors into a higherdimensional space where the linear manifolds are constructed.  ...  [7] proposed the Adaptive Quasiconformal Kernel Nearest Neighbors algorithm which warps the input space based on the local posterior probability estimates and weighted Mahalanobis distance.  ... 
doi:10.1007/s11265-008-0313-4 fatcat:dthcsdj2o5fz3jxqfysguna7gi

A kernel-based active learning strategy for content-based image retrieval

I. Daoudi, K. Idrissi
2010 2010 International Workshop on Content Based Multimedia Indexing (CBMI)  
Experimental results show that the proposed kernel-based active learning approach not only improves the retrieval performances of kernel distance without learning, but also outperforms other kernel metric  ...  The distances between user's request and database images are then learned and computed in the kernel space.  ...  Particularly, in [5] , a Quasiconformal Kernel for nearest neighbor classification is proposed which adjusts the Radial Basis function by introducing weights based on both local consistency of class labels  ... 
doi:10.1109/cbmi.2010.5529915 dblp:conf/cbmi/DaoudiI10 fatcat:bxj66epktzanzeof2olfbfssia

Challenges in KNN Classification

Shichao Zhang
2021 IEEE Transactions on Knowledge and Data Engineering  
This paper illustrates that, despite its success, there remain many challenges in KNN classification, including K computation, nearest neighbor selection, nearest neighbor search and classification rules  ...  Having established these issues, recent approaches to their resolution are examined in more detail, thereby providing a potential roadmap for ongoing KNN-related research, as well as some new classification  ...  A quasiconformal transformed kernels was applied to compute neighborhoods over which the class probabilities tend to be more homogeneous.  ... 
doi:10.1109/tkde.2021.3049250 fatcat:zeiutop4gbcfvendirz3fop5vq

Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics

Chuang Lin, Jifeng Jiang, Xuefeng Zhao, Meng Pang, Yanchun Ma
2015 Mathematical Problems in Engineering  
The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function.  ...  Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data.  ...  In this paper, we use nearest neighbor classifier for recognition. More information about LPP can be obtained from [6, 19, 21] .  ... 
doi:10.1155/2015/421671 fatcat:htlmlqak6rbhjfhw4etaku75rm

Adaptive kernel metric nearest neighbor classification

Jing Peng, D.R. Heisterkamp, H.K. Dai
Object recognition supported by user interaction for service robots  
Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose an adaptive nearest neighbor classification method to try to minimize bias.  ...  We use quasiconformal transformed kernels to compute neighborhoods over which the class probabilities tend to be more homogeneous. As a result, better classification performance can be expected.  ...  Adaptive Quasiconformal Kernel Nearest Neighbors Our adaptive quasiconformal kernel nearest neighbor (AQKNN) algorithm is motivated as follows.  ... 
doi:10.1109/icpr.2002.1047788 dblp:conf/icpr/PengHD02 fatcat:euchjqmf7ze73kd2h7l4jybywe

Breast Cancer Detection Using Optimization-Based Feature Pruning and Classification Algorithms

Somayeh Raiesdana
2021 Middle East Journal of Cancer  
Based on the results, the integrated system with a radial basis function kernel was able to extract the fewest features and result in the most accuracy (98.82%).  ...  Different validation techniques and statistical hypothesis tests (t-test and ANOVA) were used to confirm the classification results.  ...  The WOA is responsible for the feature extraction used in combination with a K-nearest neighbor (KNN) classifier to provide a supervised feature extraction process.  ... 
doi:10.30476/mejc.2020.85601.1294 doaj:c7eceddbb61242718d4d34401c4ed745 fatcat:iuztbm6tmfbotcgrbh7e67elt4

Feature-based approach to semi-supervised similarity learning

Philippe H. Gosselin, Matthieu Cord
2006 Pattern Recognition  
Neighbors.  ...  The method adapts a kernel matrix according to labels provided by the user at the end of each retrieval session.  ... 
doi:10.1016/j.patcog.2006.04.017 fatcat:t4aovw6inze7bkijekvpby7cv4

OPTIMIZATION OF LEAST SQUARES SUPPORT VECTOR MACHINE TECHNIQUE USING GENETIC ALGORITHM FOR ELECTROENCEPHALOGRAM MULTI-DIMENSIONAL SIGNALS

Farzana Kabir Ahmad, Abdullah Yousef Awwad Al-Qammaz, Yuhanis Yusof
2016 Jurnal Teknologi  
These issues have led to high dimensional problem and poor classification results.  ...  and 48.30% of arousal classification were achieved when no feature selection technique is applied on the identical classifier.  ...  Moreover, Khalili and Moradi [14] have used FFT as a feature extraction method and K-Nearest Neighbors (KNN) as a classifier in order to classify multi-class of human emotions, the accuracy result obtained  ... 
doi:10.11113/jt.v78.8842 fatcat:n6swunl5fze4xkculw6puqi2pa

Learning Image Manifolds from Local Features [chapter]

2011 Manifold Learning Theory and Applications  
neighbor face pose classification error (%) on PIE-10K subset for different algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 6.7 1-nearest neighbor face pose classification  ...  For an example in astronomy of a diffusion map incorporating a nearest-neighbor search, see Freeman, Newman, Lee, Richards, and Schafer (2009). 1. Nearest-Neighbor Search.  ... 
doi:10.1201/b11431-16 fatcat:hgcv2z3zprf4vlju2cg5wv7pqe

LDA/SVM driven nearest neighbor classification

Jing Peng, D.R. Heisterkamp, H.K. Dai
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001  
The nearest neighbor rule introduces severe bias under these conditions. We propose a locally adaptive neighborhood morphing classification method to try to minimize bias.  ...  Nearest neighbor classification relies on the assumption that class conditional probabilities are locally constant.  ...  This paper presents an adaptive metric method for effective pattern classification.  ... 
doi:10.1109/cvpr.2001.990456 dblp:conf/cvpr/PengHD01 fatcat:akfytjrfm5a7jji7x2qotrt46i
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