Belief Propagation for Probabilistic Slow Feature Analysis

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Loopy belief propagation and probabilistic image processing

The hyperparameter estimation in the maximization of the marginal likelihood in the probabilistic image processing is investigated by using the cluster variation method. The algorithms are substantially equivalent to generalized loopy belief propagations.

متن کامل

Belief Propagation in Qualitative Probabilistic Networks

Qualitative probabilistic networks (QPNs) [13] are an abstraction of in uence diagrams and Bayesian belief networks replacing numerical relations by qualitative in uences and synergies. To reason in a QPN is to nd the e ect of decision or new evidence on a variable of interest in terms of the sign of the change in belief (increase or decrease). We review our work on qualitative belief propagati...

متن کامل

Linear Response Formula and Generalized Belief Propagation for Probabilistic Inference

In probabilistic inference for signal processing, medical diagnosis, code theory, digital communication, machine learning and so on, belief propagation (BP) is one of powerful approximate methods to calculate a belief for each node within a practical computational time[1]. Recently, it has been pointed out that the BP has some mathematical structures in common with advanced mean-field (MF) meth...

متن کامل

Incremental Slow Feature Analysis

The Slow Feature Analysis (SFA) unsupervised learning framework extracts features representing the underlying causes of the changes within a temporally coherent high-dimensional raw sensory input signal. We develop the first online version of SFA, via a combination of incremental Principal Components Analysis and Minor Components Analysis. Unlike standard batch-based SFA, online SFA adapts alon...

متن کامل

Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks

Current Bayesian models for dynamic social network data have focused on modelling the influence of evolving unobserved structure on observed social interactions. However, an understanding of how observed social relationships from the past affect future unobserved structure in the network has been neglected. In this paper, we introduce a new probabilistic model for capturing this phenomenon, whi...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of the Physical Society of Japan

سال: 2017

ISSN: 0031-9015,1347-4073

DOI: 10.7566/jpsj.86.084802