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Transfer learning using a nonparametric sparse topic model

Ali Faisal, Jussi Gillberg, Gayle Leen, Jaakko Peltonen
2013 Neurocomputing  
We introduce a Bayesian generative transfer learning model which represents similarity across document collections by sparse sharing of latent topics controlled by an Indian Buffet Process.  ...  Unlike a prominent previous model, Hierarchical Dirichlet Process (HDP) based multi-task learning, our model decouples topic sharing probability from topic strength, making sharing of low-strength topics  ...  New sparse nonparametric topic model for transfer learning We present a new hierarchical Bayesian multi-task (transfer learning) model which allows flexible sharing of low-strength and high-strength topics  ... 
doi:10.1016/j.neucom.2012.12.038 fatcat:qnhlkfzdffhopj3c7jwkee332q

Statistical Model Aggregation via Parameter Matching [article]

Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang
2019 arXiv   pre-print
Exploiting tools from Bayesian nonparametrics, we develop a general meta-modeling framework that learns shared global latent structures by identifying correspondences among local model parameterizations  ...  After verifying our approach on simulated data, we demonstrate its utility in aggregating Gaussian topic models, hierarchical Dirichlet process based hidden Markov models, and sparse Gaussian processes  ...  This meta model can then be used to infer the parameters of a global model from a set of local models learned independently on private datasets.  ... 
arXiv:1911.00218v1 fatcat:hoo254bj5reppmv4jlmx3q3vde

Random Function Priors for Correlation Modeling [article]

Aonan Zhang, John Paisley
2019 arXiv   pre-print
Our model can be viewed as a generalized paintbox model Broderick13 using random functions, and can be learned efficiently with neural networks via amortized variational inference.  ...  We derive our Bayesian nonparametric method by applying a representation theorem on separately exchangeable discrete random measures.  ...  We further derived a new Bayesian nonparametric topic model to demonstrate the effectiveness of our method for learning topic correlations through deep neural networks with amortized variational posterior  ... 
arXiv:1905.03826v2 fatcat:zvmgegsfljcxna42ivehm7zpwu

Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models [article]

Alexander Terenin, Måns Magnusson, Leif Jonsson
2020 arXiv   pre-print
Based upon a representation of certain conditional distributions within an HDP, we propose a doubly sparse data-parallel sampler for the HDP topic model.  ...  We benchmark our method on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under four days.  ...  as Pitman-Yor models (Teh, 2006) , tree-based models (Hu and Boyd-Graber, 2012; Paisley et al., 2015) , embedded topic models (Dieng et al., 2020), as well as nonparametric topic models used within  ... 
arXiv:1906.02416v2 fatcat:rbsw4dud3rf7hmvj5sufd6uou4

Dependent Indian Buffet Process-based Sparse Nonparametric Nonnegative Matrix Factorization [article]

Junyu Xuan and Jie Lu and Guangquan Zhang and Richard Yi Da Xu and Xiangfeng Luo
2015 arXiv   pre-print
In this paper, we propose a nonparametric NMF framework to mitigate this issue by using dependent Indian Buffet Processes (dIBP).  ...  The method has been widely used for unsupervised learning tasks, including recommender systems (rating matrix of users by items) and document clustering (weighting matrix of papers by keywords).  ...  (2) (2) (1) (2) (2) (1) learning is to use a truncation method.  ... 
arXiv:1507.03176v1 fatcat:jdt3mihx5fftng3n4nb2hympdq

A Survey on Bayesian Nonparametric Learning

Junyu Xuan, Jie Lu, Guangquan Zhang
2019 ACM Computing Surveys  
Here, BNL can not only learn the summarised topics in a set of documents but can also adapt the number of learned topics according to the documents in the set.  ...  Section 5 reviews the model inference techniques used in this community. Sections 6 and 7 discuss the use of BNL in machine learning tasks and real-world applications.  ...  -Transfer by sharing topics. Different from factors, topics are a set of latent variables characterised by the unit summation and are used as the transferable statistical strengths between domains.  ... 
doi:10.1145/3291044 fatcat:aytdnsnrfvfnti5i64ne4icenu

Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model

Redha Touati, Max Mignotte, Mohamed Dahmane
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The dichotomic (changed/unchanged) classification map is generated in the residual space by clustering the reconstructed errors using a Gaussian mixture model.  ...  In this article, we propose a novel and simple automatic model based on multimodal anomaly feature learning in a residual space, aiming at solving the binary classification problem of temporal change detection  ...  ACKNOWLEDGMENT The authors would like to acknowledge all other researchers who made at our disposal the CD dataset in order to validate the proposed anomaly CD model.  ... 
doi:10.1109/jstars.2020.2964409 fatcat:j23jop7hmra4zio42bhhculyk4

A survey of non-exchangeable priors for Bayesian nonparametric models [article]

Nicholas J. Foti, Sinead Williamson
2012 arXiv   pre-print
Since the concept of dependent nonparametric processes was formalized by MacEachern [1], there have been a number of models proposed and used in the statistics and machine learning literatures.  ...  Such models are appropriate priors when exchangeability assumptions do not hold, and instead we want our model to vary fluidly with some set of covariates.  ...  Another approach is to use a sparse factor model [75] in a manner analogous to the image segmentation problem above.  ... 
arXiv:1211.4798v1 fatcat:asigyslavjaafiqpqov7rx2pyi

Special Section Guest Editorial: Feature and Deep Learning in Remote Sensing Applications

John E. Ball, Derek T. Anderson, Chee Seng Chan
2018 Journal of Applied Remote Sensing  
A common theme encountered was the use of nonremote sensing pretrained networks and transfer learning.  ...  Most articles used or extended convolutional neural networks (CNNs) and were application oriented, with a few providing new deep learning models and modules.  ...  A nonparametric learning algorithm using transitive matching perspective transformation. The asymptotic stability is shown to be drift-free in terms of long-term tracking.  ... 
doi:10.1117/1.jrs.11.042601 fatcat:pq3xg2sggfdtljjs3hrmp7tzdm

Statistical Methods with Applications in Data Mining: A Review of the Most Recent Works

Joaquim Fernando Pinto da Pinto da Costa, Manuel Cabral
2022 Mathematics  
This paper is a comprehensive and systematic review of these recent developments in the area of data mining.  ...  over the past decade, as the amount of data produced keeps growing exponentially and knowledge obtained from understanding data allows to make quick and informed decisions that save time and provide a  ...  Decision trees (DT) are a nonparametric supervised learning method used for classification and regression.  ... 
doi:10.3390/math10060993 fatcat:j5rz75qv6nburpq5rvsddw3cmu

Learning with Hierarchical-Deep Models

R. Salakhutdinov, J. B. Tenenbaum, A. Torralba
2013 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We introduce HD (or "Hierarchical-Deep") models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models.  ...  Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM).  ...  The proposed model allows for both: a nonparametric prior over potentially unbounded number of global topics, or higher level features, as well as a nonparametric prior that allows learning an arbitrary  ... 
doi:10.1109/tpami.2012.269 pmid:23787346 fatcat:ecdmqr225nfati75px3fbcocaa

Nonparametric Scene Parsing via Label Transfer

Ce Liu, J. Yuen, A. Torralba
2011 IEEE Transactions on Pattern Analysis and Machine Intelligence  
In this paper, we propose a novel, nonparametric approach for object recognition and scene parsing using a new technology we name label transfer.  ...  While there has been a lot of recent work on object recognition and image understanding, the focus has been on carefully establishing mathematical models for images, scenes, and objects.  ...  In the same spirit, our nonparametric label transfer system avoids modeling object appearances explicitly as our system parses a query image using the annotation of similar images in a training database  ... 
doi:10.1109/tpami.2011.131 pmid:21709305 fatcat:wcarzzyegvagva6p3uefwjccki

A Survey on Automatic Image Annotation and Retrieval

Adnan Siddiqui, Nischcol Mishra, Jitendra Singh Verma
2015 International Journal of Computer Applications  
It is a process of machine learning where low level features of images are extracted, clustered and mapped to the semantic. This can be based on training set of data.  ...  Furthermore number of models has been observed and reviewed in order to face the challenges being found in those and got rectified in proposed approach.  ...  Zhang No Segmentation, sparse feature is used Word to word matching is done. sparse feature extraction technique.  ... 
doi:10.5120/20863-3575 fatcat:rllnqwclezcnvm5j5qfbqcjhaq

Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue

Brian Quanz, Jun Huan, Meenakshi Mishra
2012 IEEE Transactions on Knowledge and Data Engineering  
of the sparse coding algorithm on synthetic data and achieve improved predictive performance on a real world chemical toxicity transfer learning task.  ...  In our paper, we point out cases where a direct application of sparse coding will lead to a failure of knowledge transfer.  ...  Advantages and Limitations of Sparse Coding for Feature Extraction in Knowledge Transfer One benefit of sparse coding for knowledge transfer comes from the viewpoint of sparse coding as a way of learning  ... 
doi:10.1109/tkde.2012.75 fatcat:rqrxs5xjcrak5ed57peyekpsga

Knowledge transfer with low-quality data: A feature extraction issue

Brian Quanz, Jun Huan, Meenakshi Mishra
2011 2011 IEEE 27th International Conference on Data Engineering  
of the sparse coding algorithm on synthetic data and achieve improved predictive performance on a real world chemical toxicity transfer learning task.  ...  In our paper, we point out cases where a direct application of sparse coding will lead to a failure of knowledge transfer.  ...  Advantages and Limitations of Sparse Coding for Feature Extraction in Knowledge Transfer One benefit of sparse coding for knowledge transfer comes from the viewpoint of sparse coding as a way of learning  ... 
doi:10.1109/icde.2011.5767917 dblp:conf/icde/QuanzHM11 fatcat:657kvgf23bb67g3b453vmi7l4e
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