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An adaptive classifier design for high-dimensional data analysis with a limited training data set
2001
IEEE Transactions on Geoscience and Remote Sensing
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the dimensionality of the multispectral data is high. ...
In this paper, we propose a self-learning and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the recognition accuracy of classifiers when ...
In other words, a self-learning and self-adapting process can then be established. This is advantageous for the analysis of high-dimensional data with limited training samples. ...
doi:10.1109/36.975001
fatcat:q6e4gqolfzbufjddskhjurw66i
A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data
[chapter]
2005
Lecture Notes in Computer Science
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In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data. ...
As seen in the experimental context, the adaptive neural classifier showed a competitive behavior with respect to the other classifiers considered; it performed a selection of the most relevant features ...
Experimental Evaluation Our experiments were designed to assess the robustness of the adaptive neural model in classifying high dimensionality data . ...
doi:10.1007/11553595_92
fatcat:erbf6uhsfzd5vnetgzpbkif7aq
Agile gesture recognition for low-power applications: customisation for generalisation
[article]
2024
arXiv
pre-print
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Additionally, it features an adaptive agile error corrector that employs few-shot learning within the feature space induced by high-dimensional kernel mappings. ...
Moreover, the challenge in data collection for individually designed hardware also hinders the generalisation of a gesture recognition model. ...
Using this tactile information, we designed and implemented a gesture classifier with an adaptive error correction mechanism. ...
arXiv:2403.15421v1
fatcat:6xk3l2irp5gbdkrlgs33mbv4xu
ADAPTIVE FEATURE SPACES FOR LAND COVER CLASSIFICATION WITH LIMITED GROUND TRUTH DATA
2004
International journal of pattern recognition and artificial intelligence
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Our experiments on two sets of remote sensing data show that both BB-BHC and BB-ECOC methods are superior to their non-adaptive versions when faced with limited data, with the BB-BHC showing a slight edge ...
The use of a simple feature extraction process also helped deal with the high dimensionality of the input space. ...
We thank Amy Neuenschander and Yangchi Chen for their help with data preparation and interpretation of results. ...
doi:10.1142/s0218001404003411
fatcat:fo4wrvamlba53o77gb2njxvvye
Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data
[chapter]
2002
Lecture Notes in Computer Science
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Our experiments on two sets of remote sensing data show that both BB-BHC and BB-ECOC methods are superior to their non-adaptive versions when faced with limited data, with the BB-BHC showing a slight edge ...
The use of a simple feature extraction process also helped deal with the high dimensionality of the input space. ...
We thank Amy Neuenschander and Yangchi Chen for their help with data preparation and interpretation of results. ...
doi:10.1007/3-540-45428-4_19
fatcat:it5mij6ajfcfjfy2beykucisny
Chained correlations for feature selection
2020
Advances in Data Analysis and Classification
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Data-driven algorithms stand and fall with the availability and quality of existing data sources. Both can be limited in high-dimensional settings (n m). ...
Interestingly, this criterion does not require direct comparisons of the initial diagnostic groups and therefore, might be suitable for settings with restricted data access. ...
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long ...
doi:10.1007/s11634-020-00397-5
fatcat:6kcl5idfvffcrk3asdqc3oqsrq
An adaptive method for combined covariance estimation and classification
2002
IEEE Transactions on Geoscience and Remote Sensing
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This is unfortunate in the analysis of high dimensional data because in the high-dimensional feature space, different classes sharing the same expected values can become separable with very little error ...
With hyperspectral data, the proposed adaptive covariance estimators can improve the classification performance dramatically with limited training samples. ...
doi:10.1109/tgrs.2002.1010895
fatcat:63ztkgfeunbvxjzjzqenn7a2um
GMM Supervectors for Limited Training Data in Hyperspectral Remote Sensing Image Classification
[chapter]
2017
Lecture Notes in Computer Science
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Supervised learning on limited training data requires either a) designing a highly capable classifier that can handle such information scarcity, or b) designing a highly informative and easily separable ...
Severely limited training data is one of the major and most common challenges in the field of hyperspectral remote sensing image classification. ...
However, both approaches, i.e. designing classifiers capable of handling limited amount of training data and feature vector dimensionality reduction are challenged in extreme cases when training data is ...
doi:10.1007/978-3-319-64698-5_25
fatcat:3d2ksdlnqzbhfbcupbz77vag6e
Error Adaptive Classifier Boosting (EACB): Leveraging Data-Driven Training Towards Hardware Resilience for Signal Inference
2015
IEEE Transactions on Circuits and Systems Part 1: Regular Papers
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Machine-learning algorithms play an important role by enabling the construction of data-driven models for inference over data that is too complex to model analytically. ...
To train an error-aware inference model, a training algorithm is presented whose hardware (memory) and energy requirements are reduced by 65 and 10 compared to previously reported algorithms (AdaBoost ...
However, in EACB a high degree of programmability in the classification model is critical for error-adaptive training. In both implementations above, the programmability is too limited. ...
doi:10.1109/tcsi.2015.2395591
fatcat:47t2dzcipvcmpj6srftyomjjne
Hybrid Dimensionality Reduction Forest with Pruning for High-Dimensional Data Classification
2020
IEEE Access
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Traditional classifier ensemble methods improve the diversity of classifiers through either dimensionality reduction or sample selection for high-dimensional data classification. ...
The classification of high-dimensional data is a challenge in machine learning. ...
[62] propose an adaptive classifier ensemble method based on spatial perception for high-dimensional data, which maintains the high performance and diversity of classifiers. ...
doi:10.1109/access.2020.2975905
fatcat:oxduxj2s6ngwvia4szjwpezjpe
Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers
[article]
2019
arXiv
pre-print
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In each classification, the training test data ratio is always set to ninety percent: ten percent. ...
Upon classification, variation between accuracies is observed and analyzed to find the degree of compatibility of each dimensionality reduction technique with each classifier and to evaluate each classifier ...
[12] for clustering which can become familiar with a lowdimensional estimation of high dimensional data accordingly can seal as an unsupervised dimensionality reduction. ...
arXiv:1912.05912v3
fatcat:qhir7yk5yzgqzenfup6iga327i
Federated Transfer Learning for EEG Signal Classification
[article]
2020
arXiv
pre-print
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Privacy concerns associated with EEG signals limit the possibility of constructing a large EEG-BCI dataset by the conglomeration of multiple small ones for jointly training machine learning models. ...
While avoiding the actual data sharing, our FTL approach achieves 2% higher classification accuracy in a subject-adaptive analysis. ...
., Ltd.) and Ruihui Zhao (Tencent Jarvis Lab) for their useful suggestions and contributions. ...
arXiv:2004.12321v2
fatcat:kg4lpgdcgnbhthjad4jiufbsf4
Adaptive Discriminant Projection for Content-based Image Retrieval
2006
18th International Conference on Pattern Recognition (ICPR'06)
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We propose an Adaptive Discrimant Projection framework which could model different data distributions based on the clustering of different classes. ...
Linear Discriminat Analysis and its variants have been widely used in CBIR applications because of their effectiveness in finding a projection that maps the original highdimensional space to a low-dimensional ...
High Dimensionality: In many data analysis application, the observed data have very high dimensionality. ...
doi:10.1109/icpr.2006.219
dblp:conf/icpr/YuT06
fatcat:7xsurlqvkrapnajlid7hibrray
Revealing representational content with pattern-information fMRI—an introductory guide
2009
Social Cognitive and Affective Neuroscience
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Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities ...
This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis ...
The training data set should be used for voxel selection (STEP 3) and classifier training (STEP 4). ...
doi:10.1093/scan/nsn044
pmid:19151374
pmcid:PMC2656880
fatcat:5uc5r3dow5cqhetsu2oyjk57yu
Digital medicine and the curse of dimensionality
2021
npj Digital Medicine
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AbstractDigital health data are multimodal and high-dimensional. ...
We posit that one of the rate-limiting factors in developing algorithms that generalize to real-world scenarios is the very attribute that makes the data exciting—their high-dimensional nature. ...
The high-dimensional nature of digital health data leaves algorithm designers with a very large raw input data stream from which to extract features for algorithm development. ...
doi:10.1038/s41746-021-00521-5
pmid:34711924
pmcid:PMC8553745
fatcat:5jlkadfnnfhl3cyjhq2wffnfui
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