Article history: Received: 11.9.2013 Received in revised form: 6.11.2013 Accepted: 26.11.2013 In a multi-sensor target tracking system, the correlation of the sensors is unknown, and the cross-covariance between the local sensors can not be calculated. To solve the problem, the multisensor covariance intersection fusion steady-state Kalman filter is proposed. The advantage of the proposed metho...

We study the problem of multimodal dimensionality reduc6 6 tion assuming that data samples can be missing at training time, and 7 7 not all data modalities may be present at application time. Maximum 8 8 covariance analysis, as a generalization of PCA, has many desirable prop9 9 erties, but its application to practical problems is limited by its need for 10 10 perfectly paired data. We overcome...

Neurons typically respond to a restricted number of stimulus features within the high-dimensional space of natural stimuli. Here we describe an explicit modelbased interpretation of traditional estimators for a neuron’s multi-dimensional feature space, which allows for several important generalizations and extensions. First, we show that traditional estimators based on the spike-triggered avera...

In machine learning, linear discriminant analysis (LDA) is a popular dimension reduction method. In this paper, we first provide a new perspective of LDA from an information theory perspective. From this new perspective, we propose a new formulation of LDA, which uses the pairwise averaged class covariance instead of the globally averaged class covariance used in standard LDA. This pairwise (av...

analysis of time series data can involve the inversion of large covariance matrices. for theclass of arma (p, q) processes there are no exact explicit expressions for these inverses, except for thema (1) process. in practice, the sample covariance matrix can be very large and inversion can becomputationally time consuming and so approximate explicit expressions for the inverse are desirable.thi...