15267641
Estimating Joint Probabilities from Marginal Ones
Estimating joint probabilities plays an important role in many data mining and machine learning tasks. In this paper we introduce two methods, minAB and prodAB, to estimate joint probabilities. Both methods are based on a light-weight structure, partition support. The core idea is to maintain the partition support of itemsets over logically disjoint partitions and then use it to estimate joint probabilities of item-sets of higher cardinalitiess. We present extensive mathematical analyses on both methods and compare their performances on synthetic datasets. We also demonstrate a case study of using the estimation methods in Apriori algorithm for fast association mining. Moreover, we explore the usefulness of the estimation methods in other mining/learning tasks. Experimental results show the effectiveness of the estimation methods.
8864863
Integrating Features from Different Sources for Music Information Retrieval
Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. In this paper, we present a clustering algorithm that integrates features from both sources to perform bimodal learning. The algorithm is tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.
37613840
Improving medical/biological data classification performance by wavelet preprocessing
Many real-world datasets contain noise which could degrade the performances of learning algorithms. Motivated from the success of wavelet denoising techniques in image data, we explore a general solution to alleviate the effect of noisy data by wavelet preprocessing for medical/biological data classification. Our experiments are divided into two categories: one is of different classification algorithms on a specific database, and the other is of a specific classification algorithm (decision tree) on different databases. The experiment results show that the wavelet denoising of noisy data is able to improve the accuracies of those classification methods, if the localities of the attributes are strong enough.
1527832
Document clustering with cluster refinement and model selection capabilities
In this paper, we propose a document clustering method that strives to achieve: (1) a high accuracy of document clustering, and (2) the capability of estimating the number of clusters in the document corpus (i.e. the model selection capability). To accurately cluster the given document corpus, we employ a richer feature set to represent each document, and use the Gaussian Mixture Model (GMM) together with the Expectation-Maximization (EM) algorithm to conduct an initial document clustering. From this initial result, we identify a set of discriminative featuresfor each cluster, and refine the initially obtained document clusters by voting on the cluster label of each document using this discriminative feature set. This self-refinement process of discriminative feature identification and cluster label voting is iteratively applied until the convergence of document clusters. On the other hand, the model selection capability is achieved by introducing randomness in the cluster initialization stage, and then discovering a value C for the number of clusters N by which running the document clustering process for a fixed number of times yields sufficiently similar results. Performance evaluations exhibit clear superiority of the proposed method with its improved document clustering and model selection accuracies. The evaluations also demonstrate how each feature as well as the cluster refinement process contribute to the document clustering accuracy.
1511154
Association-based similarity testing and its applications
This paper proposes a new similarity measure between basket datasets based on associations. The new measure is calculated from support counts using a formula inspired by information entropy. Experiments on both real and synthetic datasets show the effectiveness of the measure. This paper then investigates the applications of the similarity measure. It first studies the problem of finding a mapping between categorical database attribute sets using similarity measures. A generic approach for identifying such a mapping is proposed. The approach is implemented based on the similarity measure proposed in the paper and its performance has been evaluated and validated. Moreover, this paper also explores the applications of using the similarity measure to mine distributed datasets.
10016327
Identifying early buyers from purchase data
Market research has shown that consumers exhibit a variety of different purchasing behaviors; specifically, some tend to purchase products earlier than other consumers. Identifying such early buyers can help personalize marketing strategies, potentially improving their effectiveness. In this paper, we present a non-parametric approach to the problem of identifying early buyers from purchase data. Our formulation takes as inputs the detailed purchase information of each consumer, with which we construct a weighted directed graph whose nodes correspond to consumers and whose edges correspond to purchases consumers have in common; the edge weights indicate how frequently consumers purchase products earlier than other consumers.Identifying early buyers corresponds to the problem of finding a subset of nodes in the graph with maximum difference between the weights of the outgoing and incoming edges. This problem is a variation of the maximum cut problem in a directed graph. We provide an approximation algorithm based on semidefinite programming (SDP) relaxations pioneered by Goemans and Williamson, and analyze its performance. We apply the algorithm to real purchase data from Amazon.com, providing new insights into consumer behaviors.
122560265
Multigrid and multi-level Swendsen-Wang cuts for hierarchic graph partition
Many vision tasks can be formulated as partitioning an adjacency graph through optimizing a Bayesian posterior probability p defined on the partition-space. In this paper two approaches are proposed to generalize the Swendsen-Wang cut algorithm [A. Barbu and S.C. Zhu 2003] for sampling p. The first method is called multigrid SW-cut which runs SW-cut within a sequence of local "attentional" windows and thus simulates conditional probabilities of p in the partition space. The second method is called multi-level SW-cut which projects the adjacency graph into a hierarchical representation with each vertex in the high level graph corresponding to a sub-graph at the low level, and runs SW-cut at each level. Thus it simulates conditional probabilities of p at the higher level. Both methods are shown to observe the detailed balance equation and thus provide flexibilities in sampling the posterior probability p. We demonstrate the algorithms in image and motion segmentation with three levels (see Fig.1), and compare the speed improvement of the proposed methods.
42138946
An Evaluation of Colour Models' Performance Using Image Compression Algorithms
125037919
Perceptual Scale Space and its Applications
In this paper, we study a perceptual scale space by constructing a so-called sketch pyramid which augments the Gaussian and Laplacian pyramid representations in traditional image scale space theory. Each level of this sketch pyramid is a generic attributed graph - called the primal sketch which is inferred from the corresponding image at the same level of the Gaussian pyramid. When images are viewed at increasing resolutions, more details are revealed. This corresponds to perceptual transitions which are represented by topological changes in the sketch graph in terms of a graph grammar. We compute the sketch or perceptual pyramid by Bayesian inference upwards-downwards the pyramid using Markov chain Monte Carlo reversible jumps. We show two example applications of this perceptual scale space: (1) motion tracking of objects over scales, and (2) adaptive image displays which can efficiently show a large high resolution image in a small screen (of a PDA for example) through a selective tour of its image pyramid. Other potential applications include super resolution and multiresolution object recognition.
13451366
Comparing Different Colour Discrimination Data Sets
7665292
Example-based facial sketch generation with non-parametric sampling
In this paper, we present an example-based facial sketch system. Our system automatically generates a sketch from an input image, by learning from example sketches drawn with a particular style by an artist. There are two key elements in our system: a non-parametric sampling method and a flexible sketch model. Given an input image pixel and its neighborhood, the conditional distribution of a sketch point is computed by querying the examples and finding all similar neighborhoods. An "expected sketch image" is then drawn from the distribution to reflect the drawing style. Finally, facial sketches are obtained by incorporating the sketch model. Experimental results demonstrate the effectiveness of our techniques.