Unsupervised Rotation Factorization in Restricted Boltzmann Machines
نویسندگان
چکیده
منابع مشابه
Unsupervised spoken word retrieval using Gaussian-bernoulli restricted boltzmann machines
The objective of this work is to explore a novel unsupervised framework, using Restricted Boltzmann machines, for Spoken Word Retrieval (SWR). In the absence of labelled speech data, SWR is typically performed by matching sequence of feature vectors of query and test utterances using dynamic time warping (DTW). In such a scenario, performance of SWR system critically depends on representation o...
متن کاملUnsupervised and Supervised Visual Codes with Restricted Boltzmann Machines
Recently, the coding of local features (e.g. SIFT) for image categorization tasks has been extensively studied. Incorporated within the Bag of Words (BoW) framework, these techniques optimize the projection of local features into the visual codebook, leading to state-of-theart performances in many benchmark datasets. In this work, we propose a novel visual codebook learning approach using the r...
متن کاملDiscrete restricted Boltzmann machines
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite interactions between visible and hidden discrete variables. Examples are binary restricted Boltzmann machines and discrete näıve Bayes models. We detail the inference functions and distributed representations arising in these models in terms of configurations of projected products of simplices and ...
متن کاملSupervised Restricted Boltzmann Machines
We propose in this paper the supervised restricted Boltzmann machine (sRBM), a unified framework which combines the versatility of RBM to simultaneously learn the data representation and to perform supervised learning (i.e., a nonlinear classifier or a nonlinear regressor). Unlike the current state-of-the-art classification formulation proposed for RBM in (Larochelle et al., 2012), our model is...
متن کاملCardinality Restricted Boltzmann Machines
The Restricted Boltzmann Machine (RBM) is a popular density model that is also good for extracting features. A main source of tractability in RBM models is that, given an input, the posterior distribution over hidden variables is factorizable and can be easily computed and sampled from. Sparsity and competition in the hidden representation is beneficial, and while an RBM with competition among ...
متن کاملذخیره 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: IEEE Transactions on Image Processing
سال: 2020
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2019.2946455