Multilayered Echo State Machine: A Novel Architecture and Algorithm.

In this paper, we present a novel architecture and learning algorithm for a multilayered echo state machine (ML-ESM). Traditional echo state networks (ESNs) refer to a particular type of reservoir computing (RC) architecture. They constitute an effective approach to recurrent neural network (RNN) training, with the (RNN-based) reservoir generated randomly, and only the readout trained using a simple computationally efficient algorithm.

An unsupervised feature selection dynamic mixture model for motion segmentation.

The automatic clustering of time-varying characteristics and phenomena in natural scenes has recently received great attention. While there exist many algorithms for motion segmentation, an important issue arising from these studies concerns that for which attributes of the data should be used to cluster phenomena with a certain repetitiveness in both space and time. It is difficult because there is no knowledge about the labels of the phenomena to guide the search.