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Invariant time-series classification

2012 European Conference on machine learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012

作者： Grabocka, Josif Nanopoulos, Alexandros Schmidt-Thieme, Lars Information Systems and Machine Learning Lab. Samelsonplatz 22 Hildesheim 31141 Germany University of Eichstaett-Ingolstadt Germany

ISBN: (纸本)9783642334856

Time-series classification is a field of machine learning that has attracted considerable focus during the recent decades. The large number of time-series application areas ranges from medical diagnosis up to financial econometrics. Support Vector machines (SVMs) are reported to perform non-optimally in the domain of time series, because they suffer detecting similarities in the lack of abundant training instances. In this study we present a novel time-series transformation method which significantly improves the performance of SVMs. Our novel transformation method is used to enlarge the training set through creating new transformed instances from the support vector instances. The new transformed instances encapsulate the necessary intra-class variations required to redefine the maximum margin decision boundary. The proposed transformation method utilizes the variance distributions from the intra-class warping maps to build transformation fields, which are applied to series instances using the Moving Least Squares algorithm. Extensive experimentations on 35 time series datasets demonstrate the superiority of the proposed method compared to both the Dynamic Time Warping version of the Nearest Neighbor and the SVMs classifiers, outperforming them in the majority of the experiments. © 2012 Springer-Verlag.

关键词： Time series

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Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques 5

Scalable Event-Based Clustering of Social Media Via Record L...

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5th International AAAI Conference on Weblogs and Social Media, ICWSM 2011

作者： Reuter, Timo Cimiano, Philipp Drumond, Lucas Buza, Krisztian Schmidt-Thieme, Lars Semantic Computing CITEC University of Bielefeld Bielefeld Germany Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany

ISBN: (纸本)9781577355052

We tackle the problem of grouping content available in social media applications such as Flickr, Youtube, Panoramino etc. into clusters of documents describing the same event. This task has been referred to as event identification before. We present a new formalization of the event identification task as a record linkage problem and show that this formulation leads to a principled and highly efficient solution to the problem. We present results on two datasets derived from Flickr – *** and upcoming – comparing the results in terms of Normalized Mutual information and F-Measure with respect to several baselines, showing that a record linkage approach outperforms all baselines as well as a state-of-the-art system. We demonstrate that our approach can scale to large amounts of data, reducing the processing time considerably compared to a state-of-the-art approach. The scalability is achieved by applying an appropriate blocking strategy and relying on a Single Linkage clustering algorithm which avoids the exhaustive computation of pairwise similarities. Copyright © 2011, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Clustering algorithms

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Phantom embeddings: Using embedding space for model regularization in deep neural networks

Phantom embeddings: Using embedding space for model regulari...

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2020 Conference "learning, Knowledge, Data, Analytics", LWDA 2020

作者： Ul Islam Arif, Mofassir Jameel, Mohsan Grabocka, Josif Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany Department for Computer Science Albert-Ludwigs-University Freiburg Germany

The strength of machine learning models stems from their ability to learn complex function approximations from data;however, this strength also makes training deep neural networks challenging. Notably, the complex models tend to memorize the training data, which results in poor regularization performance on test data. The regularization techniques such as L1, L2, dropout, etc. are proposed to reduce the overfitting effect;however, they bring in additional hyperparameters tuning complexity. These methods also fall short when the inter-class similarity is high due to the underlying data distribution, leading to a less accurate model. In this paper, we present a novel approach to regularize the models by leveraging the information-rich latent embeddings and their high intra-class correlation. We create phantom embeddings from a subset of homogenous samples and use these phantom embeddings to decrease the inter-class similarity of instances in their latent embedding space. The resulting models generalize better as a combination of their embedding, regularizes them without requiring an expensive hyperparameter search. We evaluate our method on two popular and challenging image classification datasets (CIFAR and FashionMNIST) and show how our approach outperforms the standard baselines while displaying better training behavior. © 2020 by the paper's authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep neural networks

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Factorizing personalized Markov chains for next-basket recommendation 10

Factorizing personalized Markov chains for next-basket recom...

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19th International World Wide Web Conference, WWW2010

作者： Rendle, Steffen Freudenthaler, Christoph Schmidt-Thieme, Lars Department of Reasoning for Intelligence Institute of Scientific and Industrial Research Osaka University Japan Information Systems and Machine Learning Lab. Institute for Computer Science University of Hildesheim Germany Machine Learning Lab. University of Hildesheim Germany

ISBN: (纸本)9781605587998

Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC methods model sequential behavior by learning a transition graph over items that is used to predict the next action based on the recent actions of a user. In this paper, we present a method bringing both approaches together. Our method is based on personalized transition graphs over underlying Markov chains. That means for each user an own transition matrix is learned - thus in total the method uses a transition cube. As the observations for estimating the transitions are usually very limited, our method factorizes the transition cube with a pairwise interaction model which is a special case of the Tucker Decomposition. We show that our factorized personalized MC (FPMC) model subsumes both a common Markov chain and the normal matrix factorization model. For learning the model parameters, we introduce an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data. Empirically, we show that our FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization. © 2010 International World Wide Web Conference Committee (IW3C2).

关键词： Matrix algebra

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Real-time top-N recommendation in social streams 12

Real-time top-N recommendation in social streams

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6th ACM Conference on Recommender systems, RecSys 2012

作者： Diaz-Aviles, Ernesto Drumond, Lucas Schmidt-Thieme, Lars Nejdl, Wolfgang L3S Research Center University of Hannover Germany Information Systems and Machine Learning Lab University of Hildesheim Germany

ISBN: (纸本)9781450312707

The SocialWeb is successfully established, and steadily growing in terms of users, content and services. People generate and consume data in real-time within social networking services, such as Twitter, and increasingly rely upon continuous streams of messages for real-time access to fresh knowledge about current affairs. In this paper, we focus on analyzing social streams in real-time for personalized topic recommen- dation and discovery. We consider collaborative filtering as an online ranking problem and present Stream Ranking Matrix Factorization { RMFX {, which uses a pairwise approach to matrix factorization in order to optimize the personalized ranking of topics. Our novel approach follows a selective sampling strategy to perform online model updates based on active learning principles, that closely simulates the task of identifying relevant items from a pool of mostly uninteresting ones. RMFX is particularly suitable for large scale applications and experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's global trend, and it is also able to deliver highly competitive Top-N recommendations faster while using less space than Weighted Regularized Matrix Factorization (WRMF), a state-of-the-art matrix factorization technique for Collaborative Filtering, demonstrating the efficacy of our approach. Copyright © 2012 by the Association for Computing machinery, Inc. (ACM).

关键词： Collaborative filtering

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Recommender system for predicting student performance

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Procedia Computer Science 2010年第2期1卷 2811-2819页

作者： Nguyen Thai-Nghe Lucas Drumond Artus Krohn-Grimberghe Lars Schmidt-Thieme Information Systems and Machine Learning Lab University of Hildesheim 31141 Hildesheim Germany

Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning tasks such as recommending resources (e.g. papers, books,..) to the learners (students). In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance. To validate this approach, we compare recommender system techniques with traditional regression methods such as logistic/linear regression by using educational data for intelligent tutoring systems. Experimental results show that the proposed approach can improve prediction results.

关键词： Recommender systems Matrix factorization Educational data mining Student performance prediction

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Predicting RDF triples in incomplete knowledge bases with tensor factorization 12

Predicting RDF triples in incomplete knowledge bases with te...

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27th Annual ACM Symposium on Applied Computing, SAC 2012

作者： Drumond, Lucas Rendle, Steffen Schmidt-Thieme, Lars Information Systems and Machine Learning Lab. University of Hildesheim 31141 Hildesheim Germany Social Network Analysis University of Konstanz 78457 Konstanz Germany

ISBN: (纸本)9781450308571

On RDF datasets, the truth values of triples are known when they are either explicitly stated or can be inferred using logical entailment. Due to the open world semantics of RDF, nothing can be said about the truth values of triples that are neither in the dataset nor can be logically inferred. By estimating the truth values of such triples, one could discover new information from the database thus enabling to broaden the scope of queries to an RDF base that can be answered, support knowledge engineers in maintaining such knowledge bases or recommend users resources worth looking into for instance. In this paper, we present a new approach to predict the truth values of any RDF triple. Our approach uses a 3-dimensional tensor representation of the RDF knowledge base and applies tensor factorization techniques that take open world semantics into account to predict new true triples given already observed ones. We report results of experiments on real world datasets comparing different tensor factorization models. Our empirical results indicate that our approach is highly successful in estimating triple truth values on incomplete RDF datasets. © 2012 ACM.

关键词： Resource Description Framework (RDF)

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HIDRA: Head initialization across dynamic targets for robust architectures

arXiv

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arXiv 2019年

作者： Drumond, Rafael Rego Brinkmeyer, Lukas Schmidt-Thieme, Lars Grabocka, Josif University of Hildesheim Information Systems and Machine Learning Lab

The performance of gradient-based optimization strategies depends heavily on the initial weights of the parametric model. Recent works show that there exist weight initializations from which optimization procedures can find the task-specific parameters faster than from uniformly random initializations, and that such a weight initialization can be learned by optimizing a specific model architecture across similar tasks via maml (Model-Agnostic Meta-learning). Current methods are limited to populations of classification tasks that share the same number of classes due to the static model architectures used during meta-learning. In this paper, we present hidra, a meta-learning approach that enables training and evaluating across tasks with any number of target variables. We show that Model-Agnostic Meta-learning trains a distribution for all the neurons in the output layer and a specific weight initialization for the ones in the hidden layers. Hydra explores this by learning one master neuron which is used to initialize any number of output neurons for a new task. Extensive experiments on the Miniimagenet and Omniglot data sets demonstrate that hidra improves over standard approaches while generalizing to tasks with any number of target variables. Moreover, our approach is shown to robustify low-capacity models in learning across complex tasks with a high number of classes for which regular MAML fails to learn any feasible *** Codes 68 Copyright © 2019, The Authors. All rights reserved.

关键词： Neurons

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Deep Multi-Representation Model for Click-Through Rate Prediction

arXiv

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arXiv 2022年

作者： Elsayed, Shereen Schmidt-Thieme, Lars Information Systems and Machine Learning Lab Hildesheim Germany

Click-Through Rate prediction (CTR) is a crucial task in recommender systems, and it gained considerable attention in the past few years. The primary purpose of recent research emphasizes obtaining meaningful and powerful representations through mining low and high feature interactions using various components such as Deep Neural Networks (DNN), CrossNets, or transformer blocks. In this work, we propose the Deep Multi-Representation model (DeepMR) that jointly trains a mixture of two powerful feature representation learning components, namely DNNs and multi-head self-attentions. Furthermore, DeepMR integrates the novel residual with zero initialization (ReZero) connections to the DNN and the multi-head self-attention components for learning superior input representations. Experiments on three real-world datasets show that the proposed model significantly outperforms all state-of-the-art models in the task of click-through rate prediction. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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What is happening right now ... that interests me? Online topic discovery and recommendation in Twitter 12

What is happening right now ... that interests me? Online to...

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21st ACM International Conference on information and Knowledge Management, CIKM 2012

作者： Diaz-Aviles, Ernesto Drumond, Lucas Gantner, Zeno Schmidt-Thieme, Lars Nejdl, Wolfgang L3S Research Center University of Hannover Hannover Germany Information Systems and Machine Learning Lab. University of Hildesheim Hildesheim Germany

ISBN: (纸本)9781450311564

Users engaged in the Social Web increasingly rely upon continuous streams of Twitter messages (tweets) for real-time access to information and fresh knowledge about current affairs. However, given the deluge of tweets, it is a challenge for individuals to find relevant and appropriately ranked information. We propose to address this knowledge management problem by going beyond the general perspective of information finding in Twitter, that asks: "What is happening right now?", towards an individual user perspective, and ask: "What is interesting to me right now?" In this paper, we consider collaborative filtering as an online ranking problem and present RMFO, a method that creates, in real-time, user-specific rankings for a set of tweets based on individual preferences that are inferred from the user's past system interactions. Experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's global trend and Weighted Regularized Matrix Factorization (WRMF), a highly competitive state-of-the-art Collaborative Filtering technique, demonstrating the efficacy of our approach. © 2012 ACM.

关键词： Collaborative filtering

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