SVD Algorithms: APEX-like versus Subspace Methods
Neural Processing Letters
An Experimental Comparison of Three PCA Neural Networks
Neural Processing Letters
Identify Regions of Interest(ROI) for video watermark embedment with principle component analysis
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Unsupervised learning in neural computation
Theoretical Computer Science - Natural computing
Population computation of vectorial transformations
Neural Computation
Systems Analysis Modelling Simulation
Neural Network Learning Using Low-Discrepancy Sequence
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Recognizing Objects by Their Appearance Using Eigenimages
SOFSEM '00 Proceedings of the 27th Conference on Current Trends in Theory and Practice of Informatics
Generalization Error of Limear Neural Networks in Unidentifiable Cases
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Multi-Channel Subspace Mapping Using an Information Maximization Criterion
Multidimensional Systems and Signal Processing
A comparative investigation on subspace dimension determination
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay
Neural Computation
Initialization enhancer for non-negative matrix factorization
Engineering Applications of Artificial Intelligence
Neural Information Processing
Low-Complexity Principal Component Analysis for Hyperspectral Image Compression
International Journal of High Performance Computing Applications
A novel approach to neuro-fuzzy classification
Neural Networks
Analysis of Variational Bayesian Matrix Factorization
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Generalization error of linear neural networks in an empirical bayes approach
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A cellular neural network as a principal component analyzer
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Optimal learning rates for clifford neurons
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A principal components analysis neural gas algorithm for anomalies clustering
WSEAS TRANSACTIONS on SYSTEMS
Recognition of partially occluded and rotated images with a network of spiking neurons
IEEE Transactions on Neural Networks
MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
Theoretical Analysis of Bayesian Matrix Factorization
The Journal of Machine Learning Research
A modified genetic algorithm for fast training neural networks
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Global analytic solution of fully-observed variational Bayesian matrix factorization
The Journal of Machine Learning Research
The dropout learning algorithm
Artificial Intelligence
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Networks of linear units are the simplest kind of networks, where the basic questions related to learning, generalization, and self-organization can sometimes be answered analytically. We survey most of the known results on linear networks, including: 1) backpropagation learning and the structure of the error function landscape, 2) the temporal evolution of generalization, and 3) unsupervised learning algorithms and their properties. The connections to classical statistical ideas, such as principal component analysis (PCA), are emphasized as well as several simple but challenging open questions. A few new results are also spread across the paper, including an analysis of the effect of noise on backpropagation networks and a unified view of all unsupervised algorithms