Kernel-view based discriminant approach for embedded feature extraction in high-dimensional space.

In this work, we propose to use a kernel-fusion based dimensionality reduction framework for generating meaningful seismic representations from raw data. ... The availability of multiple high-dimensional observations has increased the use of machine learning techniques in a variety of fields. ... We would like to thank Yochai Ben Horin for his advice and suggestions. We are grateful to Dov Zakosky and Batia Reich for providing us with the seismic catalog of Geophysical Institute of Israel. ...

doi:10.1109/tgrs.2018.2797537 fatcat:gmjvlezshzeunnhom5ju32zzlu

Multiple Versions

Human gait recognition faces the challenge in feature extraction due to covariate conditions such as carrying and clothing, view angle, aging and other several. ... In this paper the tensor based dimensionality reduction algorithm is expressedthrough a graph embedding framework which helps to find out the hidden information lying in the lower dimensions of the manifold ... Some alternative approaches were attemptedby applying a kernel view but these techniques are data dependent and produce no result for unnoticed data. ...

doi:10.17577/ijertv4is120493 fatcat:sk37pxq4zjcltp6mzyvjybgmp4

We demonstrate the efficacy of our approach for image set classification on face and object recognition datasets. ... The independent components capture spatially local information similar to Gabor-like filters within each subspace resulting in better classification accuracy. ... (ii) The manifold hypothesis states that high dimensional data resides in a low dimensional manifold embedded in a Euclidean space. ...

doi:10.1109/icip.2016.7533198 dblp:conf/icip/KumarS16 fatcat:vpe5njldcrchnic5r54b4ecz6i

KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. ... KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with ... On the other hand, KDIsomap performs a nonlinear kernel mapping with a kernel function to extract the nonlinear features when mapping input data into some high-dimensional feature space. ...

doi:10.3390/s111009573 pmid:22163713 pmcid:PMC3231257 fatcat:ubjpf5xs2vdz7ds6gvytf2dlnu

DOAJ

They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, multi-view feature analysis. ... For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity and nonlinear separability. ... Based on such observations, it is assumed that the similarity/distance between instances in the space of subgraph features should be consistent with that in the space of a side view. ...

arXiv:1508.01023v1 fatcat:e6nscurzmbc23f26p2q42ccbrm

This paper proposes modified kernel marginal Fisher analysis (MKMFA) for feature extraction with dimensionality reduction. ... It firstly utilizes MKMFA to directly extract the low-dimensional manifold characteristics from the raw time-series signal samples in high-dimensional ambient space. ... MKMFA Algorithm KMFA is designed to capture the low-dimensional manifold characteristics embedded in high-dimensional ambient space based on graph embedding framework. ...

doi:10.1155/2016/1205868 fatcat:a3ope3rbmfhi7h2uafghhtuzuu

DOAJ

Based on multi-view analysis and graph embedding, the target features are generated to minimize the interclass discrepancy and maximize intra-class discrimination. ... Applied to the content-based retrieval task, the proposed method can be combined with Euclidean distance for the comparison of low-dimensional features. ... To preserve the structure during the embedding from the high-dimensional feature space, graph theories are adopted to learn the embedding from the multi-view feature with label information. ...

doi:10.1109/access.2019.2905641 fatcat:eqnhun66qjemfb4sru7jo6oj5y

DOAJ

In this study, a novel multple kernel FLE (MKFLE) based on general nearest feature line embedding (FLE) transformation is proposed and applied to classify hyperspectral image (HSI) in which the advantage ... However, since the conventional linear-based principle component analysis (PCA) pre-processing method in FLE cannot effectively extract the nonlinear information, the multiple kernel PCA (MKPCA) based ... Therefore, a powerful DR which can construct a high-dimensional discriminative space and preserve the manifold of discriminability in low-dimensional space is an essential step for HSI classification. ...

doi:10.3390/rs11242892 fatcat:xk6q73w7cfhurcj6l5vhkxwqdu

DOAJ Szczepanski

In this paper, we propose an approach for PVC detection and data visualization by exploiting the intrinsic geometry of the high-dimensional data using manifold learning and Support Vector Machines (SVM ... Then by incorporating training labels the method is capable of recognizing PVC patterns with comparable accuracy of kernel learning machines. ... Instead of working with points in the high-dimensional space a reduced space is found in the embedded learning process. ...

doi:10.1109/cic.2008.4749192 fatcat:5ybutcsfwngjfptana7zcbpxm4

They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, and multi-view feature analysis. ... For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity, and nonlinear separability. ... Acknowledgments This work is supported in part by NSF through grants III-1526499, CNS-1115234, and OISE-1129076, and Google Research Award. ...

doi:10.1007/s40708-015-0021-3 pmid:27747561 pmcid:PMC4883173 fatcat:rhvqh4vmeffnnoxts7esxwxlsq

DOAJ

We comprehensively analyzed the effect of kernel-based principal component analysis (PCA) on some existing high-dimensional person re-identification feature extractors to solve these problems. ... After that, we have proved that the kernel is very effective on different state-of-the-art high-dimensional feature descriptors. ... Similarly, in [14] , a high-dimensional signature composed of multiple features is projected into the low-dimensional discriminant latent space by partial least squares (PLS). ...

doi:10.35940/ijitee.k9457.09101121 fatcat:gjdz5xn6zzchlnvq2v24f2ze7i

Citation

Md Kamal Uddin, Department. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh., Amran Bhuiyan, Mahmudul Hasan, Department. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh., Department. of Computer Science and Engineering, Comilla University, Comilla, Bangladesh.. "Impact of Kernel-PCA on Different Features for Person Re-Identification." VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE 10.11 (2021) 76-81

In this paper, we present a subspace based feature extraction approach which leverages tag information to complement the page-contents of a webpage to extract highly discriminative features, with the goal ... In our approach, we consider page-text and tags as two separate views of the data, and learn a shared subspace that maximizes the correlation between the two views. ... Acknowledgement This work is supported by resources and facilities of the VA Salt Lake City Health Care System with funding support from the Consortium for Healthcare Informatics Research (CHIR), VA HSR ...

doi:10.1145/1871985.1871989 dblp:conf/cikm/TrivediRDD10 fatcat:3qzncqdrprbo3gg7aiidng6ifa

Hence, this research work analyzed the performance of three kernel feature extraction technique (Kernel Principal Component Analysis, Kernel Linear Discriminant Analysis and Kernel Independent Component ... We intend to experiment on other classifiers for face recognition system in our future work. Keywords— Biometrics, Face, Feature extraction, Kernel, KICA, KPCA, KLDA, Linear, Non-linear; ... First, images are mapped to high dimensional kernel space by using nonlinear mapping, and then ICA is applied to extract the non-linear independent components in the face images. ...

doi:10.46792/fuoyejet.v4i1.285 fatcat:cbrv6s6sbfbv3gpflxyx6mervm

Open Access

A maximum margin criterion with two levels of high dimensional mappings to kernel space is used to obtain better cross-view discrimination of the identities. ... Our method learns a discriminative space where within class samples collapse to singular points, achieving the least within class variance, and then use a maximum margin criterion over a high dimensional ... Acknowledgment: This research work is supported under Visvesvaraya PhD Scheme for Electronics and IT, by Ministry of Electronics and Information Technology (MeitY), Government of India. ...

arXiv:1910.03905v1 fatcat:joq2m2ioareate5ew3ekxrz6yy

In this paper, we design Multi-MEDA, a shift-invariant kernel for MEDA signatures that allows to reintroduce, at a kernel level, the connections between LED components that were broken with the independent ... They aggregate sets of locally extracted descriptors (LEDs) by using visual alphabets based on the marginal approximation of the LED components. ... Since the computation of such signature would result in an extremely high-dimensional vector, we shift the multidimensional modeling at a kernel in the feature space v → φ(v) and then use a kernel function ...

doi:10.1145/2324796.2324821 dblp:conf/mir/RediM12 fatcat:6gn4i7fwx5dfrglwlcirkiq5za

Multiview Kernels for Low-Dimensional Modeling of Seismic Events

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Graph Embedding based Tensor Analysis for Gait Recognition

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Learning a perceptual manifold for image set classification

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Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap

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A review of heterogeneous data mining for brain disorders [article]

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Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis

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A Video Representation Method Based on Multi-view Structure Preserving Embedding for Action Retrieval

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Multiple Kernel Feature Line Embedding for Hyperspectral Image Classification

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Manifold learning for premature ventricular contraction detection

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A review of heterogeneous data mining for brain disorder identification

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Impact of Kernel-PCA on Different Features for Person Re-Identification

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Exploiting tag and word correlations for improved webpage clustering

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Performance Evaluation of Kernel-Based Feature Extraction Techniques for Face Recognition System

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A Semi-Supervised Maximum Margin Metric Learning Approach for Small Scale Person Re-identification [article]

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Exploring two spaces with one feature

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