A Quantitative Comparison of the Performance of Three Discrete Distributed Associative Memory Models
IEEE Transactions on Computers
Pattern classification using a linear associative memory
Pattern Recognition
Linear Algebra Approach to Neural Associative Memories and Noise performance of Neural Classifiers
IEEE Transactions on Computers - Special issue on artificial neural networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Outlier Robust Gaussian Process Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Representation of Associated Data by Matrix Operators
IEEE Transactions on Computers
Recurrence enhances the spatial encoding of static inputs in reservoir networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
On the Effect of Noise on the Moore-Penrose Generalized Inverse Associative Memory
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Neural Networks
Robust extreme learning machine
Neurocomputing
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The optimal linear associative memory (OLAM) proposed by Kohonen and Ruohonen [16] is a classic neural network model widely used as a standalone pattern classifier or as a fundamental component of multilayer nonlinear classification approaches, such as the extreme learning machine (ELM) [10] and the echo-state network (ESN) [6]. In this paper, we develop an extension of OLAM which is robust to labeling errors (outliers) in the data set. The proposed model is robust to label noise not only near the class boundaries, but also far from the class boundaries which can result from mistakes in labelling or gross errors in measuring the input features. To deal with this problem, we propose the use of M-estimators, a parameter estimation framework widely used in robust regression, to compute the weight matrix operator, instead of using the ordinary least squares solution. We show the usefulness of the proposed classification approach through simulation results using synthetic and real-world data.