scholarly journals A new approach to multiple class pattern classification with random matrices

2005 ◽  
Vol 2005 (3) ◽  
pp. 165-175 ◽  
Author(s):  
Dong Q. Wang ◽  
Mengjie Zhang
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Pattern Classification ◽  
Likelihood Estimation ◽  
High Dimensional ◽  
Classification Problems ◽  
Feed Forward Neural Network ◽  
Neural Network Approach ◽  
New Approach ◽  
Multiple Class

We describe a new approach to multiple class pattern classification problems with noise and high dimensional feature space. The approach uses a random matrix X which has a specified distribution with mean M and covariance matrix rij(Σs+Σε) between any two columns of X. When Σε is known, the maximum likelihood estimators of the expectation M, correlation Γ, and covariance Σs can be obtained. The patterns with high dimensional features and noise are then classified by a modified discriminant function according to the maximum likelihood estimation results. This new method is compared with a multilayer feed forward neural network approach on nine digit recognition tasks of increasing difficulty. Both methods achieved good results for those classification tasks, but the new approach was more effective and more efficient than the neural network method for difficult problems.

scholarly journals Maximum Likelihood Angle-Range Estimation for Monostatic FDA-MIMO Radar with Extended Range Ambiguity Using Subarrays

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Kaikai Yang ◽  
Sheng Hong ◽  
Qi Zhu ◽  
Yanheng Ye
Keyword(s):  
Maximum Likelihood ◽  
Joint Angle ◽  
Likelihood Estimation ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
High Dimensional ◽  
Search Problem ◽  
Range Estimation ◽  
Detection Range ◽  
Range Ambiguity

In this paper, we consider the joint angle-range estimation in monostatic FDA-MIMO radar. The transmit subarrays are first utilized to expand the range ambiguity, and the maximum likelihood estimation (MLE) algorithm is first proposed to improve the estimation performance. The range ambiguity is a serious problem in monostatic FDA-MIMO radar, which can reduce the detection range of targets. To extend the unambiguous range, we propose to divide the transmitting array into subarrays. Then, within the unambiguous range, the maximum likelihood (ML) algorithm is proposed to estimate the angle and range with high accuracy and high resolution. In the ML algorithm, the joint angle-range estimation problem becomes a high-dimensional search problem; thus, it is computationally expensive. To reduce the computation load, the alternating projection ML (AP-ML) algorithm is proposed by transforming the high-dimensional search into a series of one-dimensional search iteratively. With the proposed AP-ML algorithm, the angle and range are automatically paired. Simulation results show that transmitting subarray can extend the range ambiguity of monostatic FDA-MIMO radar and obtain a lower cramer-rao low bound (CRLB) for range estimation. Moreover, the proposed AP-ML algorithm is superior over the traditional estimation algorithms in terms of the estimation accuracy and resolution.

scholarly journals A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhengchao Zhang ◽  
Congyuan Ji ◽  
Yineng Wang ◽  
Yanni Yang
Keyword(s):  
Neural Network ◽  
Learning Community ◽  
Mode Choice ◽  
Feature Representation ◽  
Classification Problems ◽  
Neural Network Approach ◽  
Nonlinear Fitting ◽  
Linear Utility ◽  
Utility Models ◽  
Fully Connected

Discrete choice modeling of travel modes is an essential part of traffic planning and management. Thus far, this field has been dominated by multinomial logit (MNL) models with a linear utility specification. However, deep neural networks (DNNs), owing to their powerful capacity of nonlinear fitting, are now rapidly replacing these models. This is because, by using DNNs, mode choice can be assimilated with the classification problems within the machine learning community. This article proposes a newly designed DNN framework for traffic mode choice in the style of two hidden layers. First, a local-connected layer automatically extracts an effective utility specification from the available data, and then, a fully connected layer augments the feature representation. Validated by a practical city-wide multimodal traffic dataset in Beijing, our model significantly outperforms the random utility models and simple fully connected neural network in terms of the prediction accuracy. Besides the comparison of the predictive power, we also present the interpretability of the proposed model.

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