{"title":"Training Conditional Random Fields Using Transfer Learning for Gesture Recognition","authors":"Jie Liu, Kai Yu, Yi Zhang, Yalou Huang","doi":"10.1109/ICDM.2010.31","DOIUrl":"https://doi.org/10.1109/ICDM.2010.31","url":null,"abstract":"Recently, combining Conditional Random Fields (CRF) with Neural Network has shown the success of learning high-level features in sequence labeling tasks. However, such models are difficult to train because of the increase of the parameters to tune which needs enormous of labeled data to avoid over fitting. In this paper, we propose a transfer learning framework for the sequence labeling task of gesture recognition. Taking advantage of the frame correlation, we design an unsupervised sequence model as a pseudo auxiliary task to capture the underlying information from both the labeled and unlabeled data. The knowledge learnt by the auxiliary task can be transferred to the main task of CRF with a deep architecture by sharing the hidden layers, which is very helpful for learning meaningful representation and reducing the need of labeled data. We evaluate our model under 3 gesture recognition datasets. The experimental results of both supervised learning and semi-supervised learning show that the proposed model improves the performance of the CRF with Neural Network and other baseline models.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131754284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Extensive Empirical Study on Semi-supervised Learning","authors":"Yuanyuan Guo, X. Niu, Harry Zhang","doi":"10.1109/ICDM.2010.66","DOIUrl":"https://doi.org/10.1109/ICDM.2010.66","url":null,"abstract":"Semi-supervised classification methods utilize unlabeled data to help learn better classifiers, when only a small amount of labeled data is available. Many semi-supervised learning methods have been proposed in the past decade. However, some questions have not been well answered, e.g., whether semi-supervised learning methods outperform base classifiers learned only from the labeled data, when different base classifiers are used, whether selecting unlabeled data with efforts is superior to random selection, and how the quality of the learned classifier changes at each iteration of learning process. This paper conducts an extensive empirical study on the performance of several commonly used semi-supervised learning methods when different Bayesian classifiers (NB, NBTree, TAN, HGC, HNB, and DNB) are used as the base classifier, respectively. Results on Transductive SVM and a graph-based semi-supervised learning method LLGC are also studied for comparison. The experimental results on 26 UCI datasets and 6 widely used benchmark datasets show that these semi-supervised learning methods generally do not obtain better performance than classifiers learned only from the labeled data. Moreover, for standard self-training and co-training, when selecting the most confident unlabeled instances during learning process, the performance is not necessarily better than that of random selection of unlabeled instances. We especially discovered interesting outcomes when drawing learning curves for using NB in self-training on some UCI datasets. The accuracy of the learned classifier on the testing set may fluctuate or decrease as more unlabeled instances are used. Also on the mushroom dataset, even when all the selected unlabeled instances are correctly labeled in each iteration, the accuracy on the testing set still goes down.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121338866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification","authors":"Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi","doi":"10.1109/ICDM.2010.13","DOIUrl":"https://doi.org/10.1109/ICDM.2010.13","url":null,"abstract":"We study what we call semi-defined classification, which deals with the categorization tasks where the taxonomy of the data is not well defined in advance. It is motivated by the real-world applications, where the unlabeled data may also come from some other unknown classes besides the known classes for the labeled data. Given the unlabeled data, our goal is to not only identify the instances belonging to the known classes, but also cluster the remaining data into other meaningful groups. It differs from traditional semi-supervised clustering in the sense that in semi-supervised clustering the supervision knowledge is too far from being representative of a target classification, while in semi-defined classification the labeled data may be enough to supervise the learning on the known classes. In this paper we propose the model of Double-latent-layered LDA (D-LDA for short) for this problem. Compared with LDA with only one latent variable y for word topics, D-LDA contains another latent variable z for (known and unknown) document classes. With this double latent layers consisting of y and z and the dependency between them, D-LDA directly injects the class labels into z to supervise the exploiting of word topics in y. Thus, the semi-supervised learning in D-LDA does not need the generation of pair wise constraints, which is required in most of the previous semi-supervised clustering approaches. We present the experimental results on ten different data sets for semi-defined classification. Our results are either comparable to (on one data sets), or significantly better (on the other nine data set) than the six compared methods, including the state-of-the-art semi-supervised clustering methods.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"499 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134190266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhancing Single-Objective Projective Clustering Ensembles","authors":"Francesco Gullo, C. Domeniconi, Andrea Tagarelli","doi":"10.1109/ICDM.2010.138","DOIUrl":"https://doi.org/10.1109/ICDM.2010.138","url":null,"abstract":"Projective Clustering Ensembles (PCE) has recently been formulated to solve the problem of deriving a robust projective consensus clustering from an ensemble of projective clustering solutions. PCE is formalized as an optimization problem with either a two-objective or a single-objective function, depending on whether the object-based and the feature-based representations of the clusters in the ensemble are treated separately. A major result in is that single-objective PCE outperforms two-objective PCE in terms of efficiency, at the cost of lower accuracy in consensus clustering. In this paper, we enhance the single-objective PCE formulation, with the ultimate goal of providing more effective formulations capable of reducing the accuracy gap with the two-objective counterpart, while maintaining the efficiency advantages. We provide theoretical insights into the single-objective function, and introduce two heuristics that overcome the major limitations of the previous single-objective PCE formulation. Experimental evidence has demonstrated the significance of our proposed heuristics. In fact, results have not only confirmed a far better efficiency w.r.t. two-objective PCE, but have also shown the claimed improvements in accuracy of the consensus clustering obtained by the new single-objective PCE.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114522449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
X. Chen, Bing Bai, Yanjun Qi, Qihang Lin, J. Carbonell
{"title":"Learning Preferences with Millions of Parameters by Enforcing Sparsity","authors":"X. Chen, Bing Bai, Yanjun Qi, Qihang Lin, J. Carbonell","doi":"10.1109/ICDM.2010.67","DOIUrl":"https://doi.org/10.1109/ICDM.2010.67","url":null,"abstract":"We study the retrieval task that ranks a set of objects for a given query in the pair wise preference learning framework. Recently researchers found out that raw features (e.g. words for text retrieval) and their pair wise features which describe relationships between two raw features (e.g. word synonymy or polysemy) could greatly improve the retrieval precision. However, most existing methods can not scale up to problems with many raw features (e.g. English vocabulary), due to the prohibitive computational cost on learning and the memory requirement to store a quadratic number of parameters. In this paper, we propose to learn a sparse representation of the pair wise features under the preference learning framework using the L1 regularization. Based on stochastic gradient descent, an online algorithm is devised to enforce the sparsity using a mini-batch shrinkage strategy. On multiple benchmark datasets, we show that our method achieves better performance with fast convergence, and takes much less memory on models with millions of parameters.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114624621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detecting Novel Discrepancies in Communication Networks","authors":"J. Abello, Tina Eliassi-Rad, Nishchal Devanur","doi":"10.1109/ICDM.2010.145","DOIUrl":"https://doi.org/10.1109/ICDM.2010.145","url":null,"abstract":"We address the problem of detecting characteristic patterns in communication networks. We introduce a scalable approach based on set-system discrepancy. By implicitly labeling each network edge with the sequence of times in which its two endpoints communicate, we view an entire communication network as a set-system. This view allows us to use combinatorial discrepancy as a mechanism to \"observe\" system behavior at different time scales. We illustrate our approach, called Discrepancy-based Novelty Detector (DND), on networks obtained from emails, blue tooth connections, IP traffic, and tweets. DND has almost linear runtime complexity and linear storage complexity in the number of communications. Examples of novel discrepancies that it detects are (i) asynchronous communications and (ii) disagreements in the firing rates of nodes and edges relative to the communication network as a whole.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133520679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zeno Gantner, Lucas Drumond, C. Freudenthaler, Steffen Rendle, L. Schmidt-Thieme
{"title":"Learning Attribute-to-Feature Mappings for Cold-Start Recommendations","authors":"Zeno Gantner, Lucas Drumond, C. Freudenthaler, Steffen Rendle, L. Schmidt-Thieme","doi":"10.1109/ICDM.2010.129","DOIUrl":"https://doi.org/10.1109/ICDM.2010.129","url":null,"abstract":"Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (or higher-dimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and the new-item problem, while retaining its advantages, in particular speed and predictive accuracy. We use the mapping concept to construct an attribute-aware matrix factorization model for item recommendation from implicit, positive-only feedback. Experiments on the new-item problem show that this approach provides good predictive accuracy, while the prediction time only grows by a constant factor.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132452679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improving Kernel Methods through Complex Data Mapping","authors":"Hang Zhou, F. Ramos, E. Nettleton","doi":"10.1109/ICDM.2010.33","DOIUrl":"https://doi.org/10.1109/ICDM.2010.33","url":null,"abstract":"This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function. A novel complex mapping is proposed to nonlinearly adjust the data. Optimization of the data transformation parameters is performed in two different manners. Firstly, the complex data mapping parameters and kernel hyper-parameters are selected separately, with the former guided by frequency metrics and the latter under the Bayesian framework. Next, the complex data mapping parameters and kernel hyper-parameters are optimized simultaneously in a Bayesian formulation by creating a new category of \"integrated kernel\" with the complex data mapping embedded. Experiments using Gaussian Process learning have shown that both methods improve the learning accuracy in either classification or regression tasks, with the complex mapping embedded kernel approach outperforming the separate complex mapping one.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123598045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Assessing Data Mining Results on Matrices with Randomization","authors":"Markus Ojala","doi":"10.1109/ICDM.2010.20","DOIUrl":"https://doi.org/10.1109/ICDM.2010.20","url":null,"abstract":"Randomization is a general technique for evaluating the significance of data analysis results. In randomization-based significance testing, a result is considered to be interesting if it is unlikely to obtain as good result on random data sharing some basic properties with the original data. Recently, the randomization approach has been applied to assess data mining results on binary matrices and limited types of real-valued matrices. In these works, the row and column value distributions are approximately preserved in randomization. However, the previous approaches suffer from various technical and practical shortcomings. In this paper, we give solutions to these problems and introduce a new practical algorithm for randomizing various types of matrices while preserving the row and column value distributions more accurately. We propose a new approach for randomizing matrices containing features measured in different scales. Compared to previous work, our approach can be applied to assess data mining results on different types of real-life matrices containing dissimilar features, nominal values, non-Gaussian value distributions, missing values and sparse structure. We provide an easily usable implementation that does not need problematic manual tuning as theoretically justified parameter values are given. We perform extensive experiments on various real-life datasets showing that our approach produces reasonable results on practically all types of matrices while being easy and fast to use.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130160319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Conscience On-line Learning Approach for Kernel-Based Clustering","authors":"Changdong Wang, J. Lai, Jun-Yong Zhu","doi":"10.1109/ICDM.2010.57","DOIUrl":"https://doi.org/10.1109/ICDM.2010.57","url":null,"abstract":"Kernel-based clustering is one of the most popular methods for partitioning nonlinearly separable dataset. However, exhaustive search for the global optimum is NP-hard. Iterative procedure such as k-means can be used to seek one of the local minima. Unfortunately, it is easily trapped into degenerate local minima when the prototypes of clusters are ill-initialized. In this paper, we restate the optimization problem of kernel-based clustering in an on-line learning framework, whereby a conscience mechanism is easily integrated to tackle the ill-initialization problem and faster convergence rate is achieved. Thus, we propose a novel approach termed conscience on-line learning (COLL). For each randomly taken data point, our method selects the winning prototype based on the conscience mechanism to bias the ill-initialized prototype to avoid degenerate local minima, and efficiently updates the winner by the on-line learning rule. Therefore, it can more efficiently obtain smaller distortion error than k-means with the same initialization. Experimental results on synthetic and large-scale real-world datasets, as well as that in the application of video clustering, have demonstrated the significant improvement over existing kernel clustering methods.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127976376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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