waashk/dblp huggingface dataset:
a sparsification approach for temporal graphical model decomposition. temporal causal modeling can be used to recover the causal structure among a group of relevant time series variables. several methods have been developed to explicitly construct temporal causal graphical models. however, how to best understand and conceptualize these complicated causal relationships is still an open problem. in this paper, we propose a decomposition approach to simplify the temporal graphical model. our method clusters time series variables into groups such that strong interactions appear among the variables within each group and weak (or no) interactions exist for cross-group variable pairs. specifically, we formulate the clustering problem for temporal graphical models as a regression-coefficient sparsification problem and define an interesting objective function which balances the model prediction power and its cluster structure. we introduce an iterative optimization approach utilizing the quasi-newton method and generalized ridge regression to minimize the objective function and to produce a clustered temporal graphical model. we also present a novel optimization procedure utilizing a graph theoretical tool based on the maximum weight independent set problem to speed up the quasi-newton method for a large number of variables. finally, our detailed experimental study on both synthetic and real datasets demonstrates the effectiveness of our methods.
uncoverning groups via heterogeneous interaction analysis. with the pervasive availability of web 2.0 and social networking sites, people can interact with each other easily through various social media. for instance, popular sites like del.icio.us, flickr, and youtube allow users to comment shared content (bookmark, photos, videos), and users can tag their own favorite content. users can also connect to each other, and subscribe to or become a fan or a follower of others. these diverse individual activities result in a multi-dimensional network among actors, forming cross-dimension group structures with group members sharing certain similarities. it is challenging to effectively integrate the network information of multiple dimensions in order to discover cross-dimension group structures. in this work, we propose a two-phase strategy to identify the hidden structures shared across dimensions in multi-dimensional networks. we extract structural features from each dimension of the network via modularity analysis, and then integrate them all to find out a robust community structure among actors. experiments on synthetic and real-world data validate the superiority of our strategy, enabling the analysis of collective behavior underneath diverse individual activities in a large scale.
peculiarity analysis for classifications. peculiarity-oriented mining (pom) is a new data mining method consisting of peculiar data identification and peculiar data analysis. peculiarity factor (pf) and local peculiarity factor (lpf) are important concepts employed to describe the peculiarity of points in the identification step. one can study the notions at both attribute and record levels. in this paper, a new record lpf called distance based record lpf (d-record lpf) is proposed, which is defined as the sum of distances between a point and its nearest neighbors. it is proved mathematically that d-record lpf can characterize accurately the probability density function of a continuous m-dimensional distribution. this provides a theoretical basis for some existing distance based anomaly detection techniques. more important, it also provides an effective method for describing the class conditional probabilities in the bayesian classifier. the result enables us to apply peculiarity analysis for classification problems. a novel algorithm called lpf-bayes classifier and its kernelized implementation are presented, which have some connection to the bayesian classifier. experimental results on several benchmark data sets demonstrate that the proposed classifiers are effective.
extending semi-supervised learning methods for inductive transfer learning. inductive transfer learning and semi-supervised learning are two different branches of machine learning. the former tries to reuse knowledge in labeled out-of-domain instances while the later attempts to exploit the usefulness of unlabeled in-domain instances. in this paper, we bridge the two branches by pointing out that many semi-supervised learning methods can be extended for inductive transfer learning, if the step of labeling an unlabeled instance is replaced by re-weighting a diff-distribution instance. based on this recognition, we develop a new transfer learning method, namely coitl, by extending the co-training method in semi-supervised learning. experimental results reveal that coitl can achieve significantly higher generalization and robustness, compared with two state-of-the-art methods in inductive transfer learning.
a new kernel-based classification algorithm. a new kernel-based learning algorithm called kernel affine subspace nearest point (kasnp) approach is proposed in this paper. inspired by the geometrical explanation of support vector machines (svms) and its nearest point problem in convex hulls, we extend the convex hull of each class to its corresponding affine subspace in high dimensional space induced by kernel. in two class affine subspaces, kasnp finds the nearest points and then constructs a separating hyperplane, which bisects the line segment joining them. the nearest point problem of kasnp is only an unconstrained optimal problem whose solution can be directly computed. compared with svm, kasnp avoids solving convex quadratic programming. experiments on two-spiral dataset, two uci credit datasets, and face recognition datasets show that our proposed kasnp is effective for data classification.
argumentation based constraint acquisition. efficient acquisition of constraint networks is a key factor for the applicability of constraint problem solving methods. current techniques learn constraint networks from sets of training examples, where each example is classified as either a solution or non-solution of a target network. however, in addition to this classification, an expert can usually provide arguments as to why examples should be rejected or accepted. generally speaking domain specialists have partial knowledge about the theory to be acquired which can be exploited for knowledge acquisition. based on this observation, we discuss the various types of arguments an expert can formulate and develop a knowledge acquisition algorithm for processing these types of arguments which gives the expert the possibility to input arguments in addition to the learning examples. the result of this approach is a significant reduction in the number of examples which must be provided to the learner in order to learn the target constraint network.
fine-grain perturbation for privacy preserving data publishing. recent work [12] shows that conventional privacy preserving publishing techniques based on anonymity-groups are susceptible to corruption attacks. in a corruption attack, if the sensitive information of any anonymity-group member is uncovered, then the remaining group members are at risk. in this study, we abandon anonymity-groups and hide sensitive information through perturbation on the sensitive attribute. with each record being perturbed independently, corruption attacks cannot be effectively carried out. previous anti-corruption work did not minimize information loss. this paper proposes to address this issue by allowing fine-grain privacy specification. we demonstrate the power of our approach through experiments on real medical and synthetic datasets.
outlier detection using inductive logic programming. we present a novel definition of outlier in the context of inductive logic programming. given a set of positive and negative examples, the definition aims at singling out the examples showing anomalous behavior. we note that the task here pursued is different from noise removal, and, in fact, the anomalous observations we discover are different in nature from noisy ones. we discuss pecularities of the novel approach, present an algorithm for detecting outliers, discuss some examples of knowledge mined, and compare it with alternative approaches.
improving svm classification on imbalanced data sets in distance spaces. imbalanced data sets present a particular challenge to the data mining community. often, it is the rare event that is of interest and the cost of misclassifying the rare event is higher than misclassifying the usual event. when the data is highly skewed toward the usual, it can be very difficult for a learning system to accurately detect the rare event. there have been many approaches in recent years for handling imbalanced data sets, from under-sampling the majority class to adding synthetic points to the minority class in feature space. distances between time series are known to be non-euclidean and nonmetric, since comparing time series requires warping in time. this fact makes it impossible to apply standard methods like smote to insert synthetic data points in feature spaces. we present an innovative approach that augments the minority class by adding synthetic points in distance spaces. we then use support vector machines for classification. our experimental results on standard time series show that our synthetic points significantly improve the classification rate of the rare events, and in many cases also improves the overall accuracy of svm.
maximum margin clustering with multivariate loss function. this paper presents a simple but powerful extension of the maximum margin clustering (mmc) algorithm that optimizes multivariate performance measure specifically defined for clustering, including normalized mutual in- formation, rand index and f-measure. different from previous mmc algorithms that always employ the error rate as the loss function, our formulation involves a multivariate loss function that is a non-linear combination of the individual clustering results. computationally, we propose a cutting plane algorithm to approximately solve the resulting optimization problem with a guaranteed accuracy. experimental evaluations show clear improvements in clustering performance of our method over previous maximum margin clustering algorithms.
hierarchical bayesian models for collaborative tagging systems. collaborative tagging systems with user generated content have become a fundamental element of websites such as delicious, flickr or citeulike. by sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for data mining. in this paper, we reduce the data complexity in these systems by finding meaningful topics that serve to group similar users and serve to recommend tags or resources to users. we propose a well-founded probabilistic approach that can model every aspect of a collaborative tagging system. by integrating both user information and tag information into the well-known latent dirichlet allocation framework, the developed models can be used to solve a number of important information extraction and retrieval tasks.
cross-guided clustering: transfer of relevant supervision across domains for improved clustering. lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. we investigate if supervision can be automatically transferred to a clustering task in a target domain, by providing a relevant supervised partitioning of a dataset from a different source domain. the target clustering is made more meaningful for the human user by trading off intrinsic clustering goodness on the target dataset for alignment with relevant supervised partitions in the source dataset, wherever possible. we propose a cross-guided clustering algorithm that builds on traditional k-means by aligning the target clusters with source partitions. the alignment process makes use of a cross-domain similarity measure that discovers hidden relationships across domains with potentially different vocabularies. using multiple real-world datasets, we show that our approach improves clustering accuracy significantly over traditional k-means.
mining peculiarity groups in day-by-day behavioral datasets. behavior mining is one of the most important issues in data mining. the growing interest in the study of behavior mining has been credited to the availability of a large amount of individual behavioral data. some objects containing common behavioral patterns in the dataset are dramatically different from other individual objects and show their peculiarities. it is very important for behavior analysis to mine these peculiar objects' groups as this has great potential in practice. however, to the best of our knowledge, it has not been explored before. in this paper, we identify this interesting and practical problem of behavior mining: mining peculiarity groups and defining a measurement of the degree of peculiarity. as the first attempt to tackle the problem, we present a set-value-oriented day-by-day behavioral data expression mode considering that daily behaviors with respect to an object should be recorded as a set of behaviors, and devise a peculiarity group mining algorithm in view of the set-value-oriented data expression which cannot be very well handled by existing methods. furthermore, we show that our method is practical and efficient using real datasets.
beyond banditron: a conservative and efficient reduction for online multiclass prediction with bandit setting model. in this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. aiming at learning from partial feedback of online classification results, i.e. “true” when the predicting label is right or “false” when the predicting label is wrong, this new kind of problems arouses much of researchers’ interest due to its close relations to real world internet applications and human cognitive procedure. while some algorithms have been brought forward, we propose a novel algorithm to deal with such problems. first, we reduce the multiclass prediction problem to binary based on conservative one-versus-all others reduction scheme; then online passive-aggressive algorithm is embedded as binary learning algorithm to solve the reduced problem. also we derive a pleasing cumulative mistake bound for our algorithm and a time complexity bound linear to the sample size. further experimental evaluation on several real world multiclass datasets including rcv1, mnist, 20 newsgroups and usps shows that our method outperforms the existing algorithms with a great improvement.
multi-document summarization by information distance. fast changing knowledge on the internet can be acquired more efficiently with the help of automatic document summarization and updating techniques. this paper described a novel approach for multi-document update summarization. the best summary is defined to be the one which has the minimum information distance to the entire document set. the best update summary has the minimum conditional information distance to a document cluster given that a prior document cluster has already been read. experiments on the duc 2007 dataset and the tac 2008 dataset have proved that our method closely correlates with the human summaries and outperforms other programs such as lexrank in many categories under the rouge evaluation criterion.
knowledge discovery from citation networks. knowledge discovery from scientific articles has received increasing attentions recently since huge repositories are made available by the development of the internet and digital databases. in a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: \emph{document itself} and \emph{a citation of other documents}. in the existing topic models, little effort is made to differentiate these two roles. we believe that the topic distributions of these two roles are different and related in a certain way. in this paper we propose a \emph{bernoulli process topic}~(bpt) model which models the corpus at two levels: \emph{document level} and \emph{citation level}. in the bpt model, each document has two different representations in the latent topic space associated with its roles. moreover, the multi-level hierarchical structure of the citation network is captured by a generative process involving a bernoulli process. the distribution parameters of the bpt model are estimated by a variational approximation approach. in addition to conducting the experimental evaluations on the document modeling task, we also apply the bpt model to a well known scientific corpus to discover the latent topics. the comparisons against state-of-the-art methods demonstrate a very promising performance.
scalable classification in large scale spatiotemporal domains applied to voltage-sensitive dye imaging. we present an approach for learning models that obtain accurate classification of large scale data objects, collected in spatiotemporal domains. the model generation is structured in three phases: pixel selection (spatial dimension reduction), spatiotemporal features extraction and feature selection. novel techniques for the first two phases are presented, with two alternatives for the middle phase. model generation based on the combinations of techniques from each phase is explored. the introduced methodology is applied on datasets from the voltage-sensitive dye imaging (vsdi) domain, where the generated classification models successfully decode neuronal population responses in the visual cortex of behaving animals. vsdi currently is the best technique enabling simultaneous high spatial (10,000 points) and temporal (10 ms or less) resolution imaging from neuronal population in the cortex. we demonstrate that not only our approach is scalable enough to handle computationally challenging data, but it also contributes to the neuroimaging field of study with its decoding abilities.
significance of episodes based on minimal windows. discovering episodes, frequent sets of events from a sequence has been an active field in pattern mining. traditionally, a level-wise approach is used to discover all frequent episodes. while this technique is computationally feasible it may result in a vast number of patterns, especially when low thresholds are used. in this paper we propose a new quality measure for episodes. we say that an episode is significant if the average length of its minimal windows deviates greatly when compared to the expected length according to the independence model. we can apply this measure as a post-pruning step to test whether the discovered frequent episodes are truly interesting and consequently to reduce the number of output. as a main contribution we introduce a technique that allows us to compute the distribution of lengths of minimal windows using the independence model. such a computation task is surpisingly complex and in order to solve it we compute the distribution iteratively starting from simple episodes and progressively moving towards the more complex ones. in our experiments we discover candidate episodes that have a sufficient amount of minimal windows and test each candidate for significance. the experimental results demonstrate that our approach finds significant episodes while ignoring uninteresting ones.
naive bayes classification of uncertain data. traditional machine learning algorithms assume that data are exact or precise. however, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, and repeated measurements, etc. with uncertainty, the value of each data item is represented by a probability distribution function (pdf). in this paper, we propose a novel naive bayes classification algorithm for uncertain data with a pdf. our key solution is to extend the class conditional probability estimation in the bayes model to handle pdf’s. extensive experiments on uci datasets show that the accuracy of naive bayes model can be improved by taking into account the uncertainty information.
dirichlet mixture allocation for multiclass document collections modeling. topic model, latent dirichlet allocation (lda), is an effective tool for statistical analysis of large collections of documents. in lda, each document is modeled as a mixture of topics and the topic proportions are generated from the unimodal dirichlet distribution prior. when a collection of documents are drawn from multiple classes, this unimodal prior is insufficient for data fitting. to solve this problem, we exploit the multimodal dirichlet mixture prior, and propose the dirichlet mixture allocation (dma). we report experiments on the popular tdt2 corpus demonstrating that dma models a collection of documents more precisely than lda when the documents are obtained from multiple classes.
semi-markov kmeans clustering and activity recognition from body-worn sensors. subsequence clustering aims to find patterns that appear repeatedly in time series data. we introduce a novel subsequence clustering technique that we call semi-markov kmeans clustering. the clustering results in ideal examples of the repeating patterns and in labeled segmentations that can be used as training data for sophisticated discriminative methods like max-margin semi-markov models. we are applying the new clustering technique to activity recognition from body-worn sensors by showing how it can enable a system to learn from data that is only annotated by an ordered list of activity types that have been undertaken. this kind of annotation, unlike a detailed segmentation of the sensor data, is easily provided by a non-expert user. we show that we can achieve equally good results using only an ordered list of activity types for training as when using a full detailed labeled segmentation.
discovering excitatory networks from discrete event streams with applications to neuronal spike train analysis. mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-computer interaction modeling. we focus in this paper on temporal models representable as excitatory networks where all connections are stimulative, rather than inhibitory. through this emphasis on excitatory networks, we show how they can be learned by creating bridges to frequent episode mining. specifically, we show that frequent episodes help identify nodes with high mutual information relationships and which can be summarized into a dynamic bayesian network (dbn). to demonstrate the practical feasibility of our approach, we show how excitatory networks can be inferred from both mathematical models of spiking neurons as well as real neuroscience datasets.
a bootstrap approach to eigenvalue correction. eigenvalue analysis is an important aspect in many data modeling methods. unfortunately, the eigenvalues of the sample covariance matrix (sample eigenvalues) are biased estimates of the eigenvalues of the covariance matrix of the data generating process (population eigenvalues). we present a new method based on bootstrapping to reduce the bias in the sample eigenvalues: the eigenvalue estimates are updated in several iterations, where in each iteration synthetic data is generated to determine how to update the population eigenvalue estimates. comparison of the bootstrap eigenvalue correction with a state of the art correction method by karoui shows that depending on the type of population eigenvalue distribution, sometimes the karoui method performs better and sometimes our bootstrap method.