{"title":"Detection of Unnatural Movement Using Epitomic Analysis","authors":"Wooyoung Kim, James M. Rehg","doi":"10.1109/ICMLA.2008.138","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.138","url":null,"abstract":"Epitomic analysis, a recent statistical approach to form a generative model, has been applied to image, video and audio processing applications. We apply the epitomic analysis to motion capture data and define it as a motion epitome, a probabilistic model representing a finite set of primitive movements which retain various lengths of local dynamics. We review the generation, inference and learning procedures of an epitome, adapt them for motion capture data and utilize the epitomic analysis to detect unnatural movements given only positive (natural) training data. We introduce a multi-resolution of motion epitomes as well as a full body and an ensemble of epitomes, then present experimental results and compare the performance with other conventional classification methods, including Hidden Markov Models and Switching Linear Dynamic Systems.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126061113","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 Weighted Distance Measure for Calculating the Similarity of Sparsely Distributed Trajectories","authors":"Pekka Siirtola, P. Laurinen, J. Röning","doi":"10.1109/ICMLA.2008.118","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.118","url":null,"abstract":"This article presents a method for the calculating similarity of two trajectories. The method is especially designed for a situation where the points of the trajectories are distributed sparsely and at non-equidistant intervals. The proposed method is based on giving different weights to different points: points that are close to each other get smaller weights than the points that do not have neighbors nearby. The effectiveness of the method was tested with 12 data sets generated from two benchmark data sets. The classifying accuracy of the proposed similarity measure was compared with three other methods, such as dynamic time warping, and it was noted that the new proposed method classifies instances mainly more accurately and faster than the other three methods.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126422692","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}
Johannes Mohr, Sambu Seo, I. Puls, A. Heinz, K. Obermayer
{"title":"Target Selection: A New Learning Paradigm and Its Application to Genetic Association Studies","authors":"Johannes Mohr, Sambu Seo, I. Puls, A. Heinz, K. Obermayer","doi":"10.1109/ICMLA.2008.58","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.58","url":null,"abstract":"In this work, a new learning paradigm called target selection is proposed, which can be used to test for associations between a single genetic variable and a multidimensional, quantitative phenotype. In target selection, the task of a learning machine is to chose one out of several nominal target variables, as well as a probabilistic classification function for the selected target. For this new paradigm, a cost function is derived from the concept of mutual information and a learning algorithm is suggested. The significance of the generalization performance of the model learned using target selection is tested using a label permutation test. Here, the proposed target selection paradigm is applied to a genomic imaging study.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121795568","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 Improved Generalized Discriminant Analysis for Large-Scale Data Set","authors":"Weiya Shi, Yue-Fei Guo, Cheng Jin, X. Xue","doi":"10.1109/ICMLA.2008.41","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.41","url":null,"abstract":"In order to overcome the computation and storage problem for large-scale data set, an efficient iterative method of generalized discriminant analysis is proposed. Because sample vectors cannot explicitly be denoted in kernel space, some mathematical tricks are firstly used to transform the kernel matrix. Then, the columns of transformed matrix are used for iterative algorithm to extract nonlinear discriminant vectors. The proposed method reduces space complexity from O(m2) to O(m) and its effectiveness is validated from experimental results.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122135249","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":"Speech Emotion Recognition Using Canonical Correlation Analysis and Probabilistic Neural Network","authors":"Ling Cen, W. Ser, Z. Yu","doi":"10.1109/ICMLA.2008.85","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.85","url":null,"abstract":"In this paper, automatic identification of emotional states from human speech is addressed. While several papers have been published in the literature on speech emotion recognition, the features used are taken or modified from those used for speech recognition purposes. However, not all features used for speech recognition are of equal importance for emotion recognition. This paper addresses this issue and proposes a systematic method on feature selection for emotion recognition from speech signals. The idea is to work on a well-selected small feature set and use it to remove irrelevant information. Specifically, the proposed method uses the similar idea of the Canonical Correlation Analysis (CCA) to estimate the linear relationship between the various features and the emotional states. The outcome is a set of features that are of most relevance to the emotions. Experiments have been conducted using the LDC database and with the use of the Probabilistic Neural Network (PNN) as the classification method. The results obtained show that, comparable accuracies can be obtained for the emotional states tested with the use of only about 30% of the features considered. This implies that the computational load can be reduced greatly too.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126503473","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":"Multi-stage Learning of Linear Algebra Algorithms","authors":"V. Eijkhout, E. Fuentes","doi":"10.1109/ICMLA.2008.10","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.10","url":null,"abstract":"In evolving applications, there is a need for the dynamic selection of algorithms or algorithm parameters. Such selection is hardly ever governed by exact theory, so intelligent recommender systems have been proposed. In our application area, the iterative solution of linear systems of equations, the recommendation process is especially complicated, since the classes have a multi-dimensional structure. We discuss different strategies of recommending the different components of the algorithms.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131831387","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":"Farthest Centroids Divisive Clustering","authors":"Haw-ren Fang, Y. Saad","doi":"10.1109/ICMLA.2008.141","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.141","url":null,"abstract":"A method is presented to partition a given set of data entries embedded in Euclidean space by recursively bisecting clusters into smaller ones. The initial set is subdivided into two subsets whose centroids are farthest from each other, and the process is repeated recursively on each subset. An approximate algorithm is proposed to solve the original integer programming problem which is NP-hard. Experimental evidence shows that the clustering method often outperforms a standard spectral clustering method, albeit at a slightly higher computational cost. The paper also discusses improvements of the standard K-means algorithm. Specifically, the clustering quality resulting from the K-means technique can be significantly enhanced by using the proposed algorithm for its initialization.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124673986","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":"Extraction of Meaningful Rules in a Medical Database","authors":"S. Suh, S. I. Saffer, Naveen Kumar Adla","doi":"10.1109/ICMLA.2008.123","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.123","url":null,"abstract":"Clustering enhances the value of existing databases by revealing rules in the data. These rules are useful for understanding trends, making predictions of future events from historical data, or synthesizing data records into meaningful clusters. Through clustering are similar data items grouped together to form clusters. Clustering algorithms usually employ a distance metric based (e.g., Euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than points in different partitions. In this paper, we study clustering algorithms for data with categorical attributes. Instead of using traditional clustering algorithms that use distances between points for clustering which is not an appropriate concept for Boolean and categorical attributes, we propose a novel concept of HAC (hierarchy of attributes and concepts) to measure the similarity/proximity between a pair of data points. In this study, HAC will be used as an aid to represent medical domain knowledge substructures to simplify the generation process of the databases through clustering. As a result, the research will identify interesting relationships and patterns among the data, and represent them in the form of association rules.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115044273","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":"Dimension Reduction via Unsupervised Learning Yields Significant Computational Improvements for Support Vector Machine Based Protein Family Classification","authors":"B. Webb-Robertson, M. Matzke, C. Oehmen","doi":"10.1109/ICMLA.2008.120","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.120","url":null,"abstract":"Reducing the dimension of vectors used in training support vector machines (SVMs) results in a proportional speedup in training time. For large-scale problems this can make the difference between tractable and intractable training tasks. However, it is critical that classifiers trained on reduced datasets perform as reliably as their counterparts trained on high-dimensional data. We assessed principal component analysis (PCA) and sequential project pursuit (SPP) as dimension reduction strategies in the biology application of classifying proteins into well-defined functional dasiafamiliespsila (SVM-based protein family classification) by their impact on run-time, sensitivity and selectivity. Homology vectors of 4352 elements were reduced to approximately 2% of the original data size using PCA and SPP without significantly affecting accuracy, while leading to approximately a 28-fold speedup in run-time.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131148886","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 Investigation of Non-Uniform Error Cost Function Design in Automatic Speech Recognition","authors":"Qiang Fu, B. Juang","doi":"10.1109/ICMLA.2008.82","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.82","url":null,"abstract":"The classical Bayes decision theory [3] is the foundation of statistical pattern recognition. In [4], we have addressed the issue of non-uniform error criteria in statistical pattern recognition, and generalized the Bayes decision theory for pattern recognition tasks where errors over different classes have varying degrees of significance. We further introduced the weighted minimum classification error (MCE) method for a practical design of a statistical pattern recognition system to achieve empirical optimality when non-uniform error criteria are prescribed. However, one key issue in the weighted MCE method, the methodology of building a suitable non-uniform error cost function given the userpsilas requirements, has not been addressed yet. In this paper, we propose some viable techniques for the design of the non-uniform error cost function in the context of automatic speech recognition (ASR) according to different training scenarios. The experimental results on the TIDIGITS database [8] are presented to demonstrate the effectiveness of our methodologies.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133887818","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}