TAPCA: time adaptive self-organizing maps for adaptive principal components analysis
نویسندگان
چکیده
In this paper, we propose a neural network called Time Adaptive Principal Components Analysis (TAPCA) which is composed of a number of Time Adaptive SelfOrganizing Map (TASOM) networks. Each TASOM in TAPCA network estimates one eigenvector of tlie correlation matrix of input vectors entered so far, without having to calculate the correlation matrix. This estimation is done in an online fashion. The input distribution can be nonstationary, too. The eigenvectors appear in order of importance: the first TASOM calculates tlie eigenvector corresponding to the largest eigenvalue of the correlation matrix, and so on. Tlie TAPCA network is tested in stationary environments, and is compared with tlie eigendecomposition (ED) method and GeneraliLed Hebbian Algorithm (GHA) network. It performs better tlian both methods and needs fewer samples to converge. It is also tested in nonstationary environments, where it automatically tolerates translation, rotation, scaling, and a change in the shape of distribution.
منابع مشابه
The Time Adaptive Self Organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملA principal components analysis self-organizing neural network model and computational experiment
We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network. KeywordsSelf-organization; Principal component analysis; Competitive lear...
متن کاملThe Principal Components Analysis Self-Organizing Map
We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.
متن کاملAn Approach to Collaboration of Growing Self-Organizing Maps and Adaptive Resonance Theory Maps
Collaboration of growing self-organizing maps (GSOM) and adaptive resonance theory maps (ART) is considered through traveling sales-person problems (TSP).The ART is used to parallelize the GSOM: it divides the input space of city positions into subspaces automatically. One GSOM is allocated to each subspace and grows following the input data. After all the GSOMs grow sufficiently they are conne...
متن کاملADAPTIVE ORDERED WEIGHTED AVERAGING FOR ANOMALY DETECTION IN CLUSTER-BASED MOBILE AD HOC NETWORKS
In this paper, an anomaly detection method in cluster-based mobile ad hoc networks with ad hoc on demand distance vector (AODV) routing protocol is proposed. In the method, the required features for describing the normal behavior of AODV are defined via step by step analysis of AODV and independent of any attack. In order to learn the normal behavior of AODV, a fuzzy averaging method is used fo...
متن کامل