scholarly journals Autonomous EBSD Pattern Classification Performance with Changing Acquisition Parameters

2021 ◽  
Vol 27 (S1) ◽  
pp. 2490-2493
Author(s):  
Kevin Kaufmann ◽  
Kenneth Vecchio
Keyword(s):  
Pattern Classification ◽  
Classification Performance ◽  
Ebsd Pattern

scholarly journals Nonnegative Signal Decomposition with Supervision

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Teng Li ◽  
Huan Chang ◽  
Jun Wu
Keyword(s):  
Objective Function ◽  
Image Classification ◽  
Pattern Classification ◽  
Optimization Problem ◽  
Real Data ◽  
Classification Performance ◽  
Signal Decomposition ◽  
Weak Supervision ◽  
Training Samples ◽  
Novel Algorithm

This paper presents a novel algorithm to numerically decompose mixed signals in a collaborative way, given supervision of the labels that each signal contains. The decomposition is formulated as an optimization problem incorporating nonnegative constraint. A nonnegative data factorization solution is presented to yield the decomposed results. It is shown that the optimization is efficient and decreases the objective function monotonically. Such a decomposition algorithm can be applied on multilabel training samples for pattern classification. The real-data experimental results show that the proposed algorithm can significantly facilitate the multilabel image classification performance with weak supervision.

scholarly journals Discriminative non-negative representation based classifier for image recognition

2021 ◽  
Vol 15 ◽  
pp. 174830262110449
Author(s):  
Kai-Jun Hu ◽  
He-Feng Yin ◽  
Jun Sun
Keyword(s):  
Pattern Classification ◽  
State Of The Art ◽  
Source Code ◽  
Classification Performance ◽  
Training Data ◽  
Practical Applications ◽  
The Past ◽  
Benchmark Datasets ◽  
Classification Tasks ◽  
Negative Representation

During the past decade, representation based classification method has received considerable attention in the community of pattern recognition. The recently proposed non-negative representation based classifier achieved superb recognition results in diverse pattern classification tasks. Unfortunately, discriminative information of training data is not fully exploited in non-negative representation based classifier, which undermines its classification performance in practical applications. To address this problem, we introduce a decorrelation regularizer into the formulation of non-negative representation based classifier and propose a discriminative non-negative representation based classifier for pattern classification. The decorrelation regularizer is able to reduce the correlation of representation results of different classes, thus promoting the competition among them. Experimental results on benchmark datasets validate the efficacy of the proposed discriminative non-negative representation based classifier, and it can outperform some state-of-the-art deep learning based methods. The source code of our proposed discriminative non-negative representation based classifier is accessible at https://github.com/yinhefeng/DNRC .

Making an Electronic Nose Versatile

2011 ◽  
pp. 78-101
Author(s):  
Nabarun Bhattacharyya ◽  
Bipan Tudu ◽  
Rajib Bandyopadhyay
Keyword(s):  
Pattern Classification ◽  
Electronic Nose ◽  
Incremental Learning ◽  
Learning Algorithms ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Machine Olfaction ◽  
Entire Dataset

Because of these factors, it is necessary to make the system flexible in such a way that the system is able to update an existing classifier without affecting the classification performance on old data, and such classifiers should have the property as being both stable and plastic. Conventional pattern classification algorithms require the entire dataset during training, and thereby fail to meet the criteria of being plastic and stable at the same time. The incremental learning algorithms possess these features, and thus, the electronic nose systems become extremely versatile when equipped with these classifiers. In this chapter, the authors describe different incremental learning algorithms for machine olfaction.

Performance Comparison of Classification Methods for Surface EMG-Based Human-Machine Interface

2015 ◽  
Vol 7 (4) ◽  
pp. 47-56 ◽  
Author(s):  
Chun Yang ◽  
Jinyi Long ◽  
Hao Wang
Keyword(s):  
Pattern Classification ◽  
Information Gain ◽  
Surface Emg ◽  
Classification Performance ◽  
Performance Comparison ◽  
Classification Methods ◽  
Wrist Flexion ◽  
Linear Classifiers ◽  
Feature Selection Approach

Reliable control of assistive devices through surface electromyography (sEMG) based human-machine interfaces (HMIs) requires accurate classification of multi-channel sEMG. The design of effective pattern classification methods is one of the main challenges for sEMG-based HMIs. In this paper, the authors compared comprehensively the performance of different linear and nonlinear classifiers for the pattern classification of sEMG with respect to three pairs of upper-limb motions (i.e., hand close vs. hand open, wrist flexion vs. wrist extension, and forearm pronation vs. forearm supination). A feature selection approach based on information gain was also performed to reduce the muscle channels. Overall, the results showed that the linear classifiers produce slightly better classification performance, with or without the muscle channel selection.

scholarly journals Maximum Neighborhood Margin Discriminant Projection for Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Jianping Gou ◽  
Yongzhao Zhan ◽  
Min Wan ◽  
Xiangjun Shen ◽  
Jinfu Chen ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Pattern Classification ◽  
Pattern Discrimination ◽  
Classification Performance ◽  
Local Information ◽  
Experimental Results ◽  
High Dimensional ◽  
Face Database ◽  
Class Information ◽  
Discriminant Projection

We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.

Pattern Classification Based on Self-Organizing Feature Mapping Neural Network

2013 ◽  
Vol 448-453 ◽  
pp. 3645-3649 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Classification Performance ◽  
Adjustment Process ◽  
Data Sets ◽  
Two Dimensional ◽  
Sample Data ◽  
Sofm Network ◽  
Traditional Pattern

Traditional pattern classification methods are not always efficient because sample data sets are sometimes incomplete and there are exceptions and counter examples. In this paper, SOFM neural network is applied in pattern classification of two-dimensional vectors after analysis of its structure and algorithm. The method to establish SOFM network via MATLAB7.0 is introduced before the network is applied to classify two-dimensional vectors. The adjustment process of weight vectors together with classification performance of SOFM model are also tested in the condition of different number of training steps. The simulation results show that the classification approach based on SOFM model is effective because of its fast speed, high accuracy and strong generalization ability.

scholarly journals Label consistent transform learning for pattern classification

2019 ◽  
Vol 13 ◽  
pp. 174830261988139
Author(s):  
He-Feng Yin ◽  
Xiao-Jun Wu
Keyword(s):  
Image Processing ◽  
Pattern Classification ◽  
Dictionary Learning ◽  
Optimization Problem ◽  
Classification Performance ◽  
Alternative Strategy ◽  
Learning Approaches ◽  
Training Time ◽  
Practical Applications ◽  
Consistency Constraint

Transform learning has been successfully applied to various image processing tasks in recent years. Nevertheless, transform learning learns the representation in an unsupervised fashion. To make transform learning suitable for pattern classification, we introduce a label consistency constraint into transform learning and propose a new label consistent transform learning to enhance the classification performance of transform learning. The resulting optimization problem can be solved elegantly by employing the alternative strategy. Experimental results on publicly available databases demonstrate that label consistent transform learning outperforms several dictionary learning approaches and the recently proposed discriminative transform learning. More importantly, label consistent transform learning has the least training time which has the potential in practical applications.

The Sound of Enemies and Friends in the Neighborhood

2011 ◽  
Vol 58 (6) ◽  
pp. 454-463 ◽  
Author(s):  
Diane Pecher ◽  
Inge Boot ◽  
Saskia van Dantzig ◽  
Carol J. Madden ◽  
David E. Huber ◽  
...  
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Neighborhood Effects ◽  
Classification Performance ◽  
Visual Word ◽  
Orthographic Neighborhood ◽  
Semantic Classification ◽  
Semantic Class ◽  
Orthographic Neighbors ◽  
Target Words

Previous studies (e.g., Pecher, Zeelenberg, & Wagenmakers, 2005) found that semantic classification performance is better for target words with orthographic neighbors that are mostly from the same semantic class (e.g., living) compared to target words with orthographic neighbors that are mostly from the opposite semantic class (e.g., nonliving). In the present study we investigated the contribution of phonology to orthographic neighborhood effects by comparing effects of phonologically congruent orthographic neighbors (book-hook) to phonologically incongruent orthographic neighbors (sand-wand). The prior presentation of a semantically congruent word produced larger effects on subsequent animacy decisions when the previously presented word was a phonologically congruent neighbor than when it was a phonologically incongruent neighbor. In a second experiment, performance differences between target words with versus without semantically congruent orthographic neighbors were larger if the orthographic neighbors were also phonologically congruent. These results support models of visual word recognition that assume an important role for phonology in cascaded access to meaning.

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