32009068
Finding biclusters by random projections
Given a matrix X composed of symbols, a bicluster is a submatrix of X obtained by removing some of the rows and some of the columns of X in such a way that each row of what is left reads the same string. In this paper, we are concerned with the problem of finding the bicluster with the largest area in a large matrix X. The problem is first proved to be NP-complete. We present a fast and efficient randomized algorithm that discovers the largest bicluster by random projections. A detailed probabilistic analysis of the algorithm and an asymptotic study of the statistical significance of the solutions are given. We report results of extensive simulations on synthetic data.
18229979
A Compression-Boosting Transform for Two-Dimensional Data
We introduce a novel invertible transform for two- dimensional data which has the objective of reordering the matrix so it will improve its (lossless) compression at later stages. The transform requires to solve a computationally hard problem for which a randomized algorithm is used. The inverse transform is fast and can be implemented in linear time in the size of the matrix. Preliminary experimental results show that the reordering improves the compressibility of digital images.
6653256
Domain-constrained semi-supervised mining of tracking models in sensor networks
Accurate localization of mobile objects is a major research problem in sensor networks and an important data mining application. Specifically, the localization problem is to determine the location of a client device accurately given the radio signal strength values received at the client device from multiple beacon sensors or access points. Conventional data mining and machine learning methods can be applied to solve this problem. However, all of them require large amounts of labeled training data, which can be quite expensive. In this paper, we propose a probabilistic semi supervised learning approach to reduce the calibration effort and increase the tracking accuracy. Our method is based on semi-supervised conditional random fields which can enhance the learned model from a small set of training data with abundant unlabeled data effectively. To make our method more efficient, we exploit a Generalized EM algorithm coupled with domain constraints. We validate our method through extensive experiments in a real sensor network using Crossbow MICA2 sensors. The results demonstrate the advantages of methods compared to other state-of-the-art object-tracking algorithms.
7514161
An Incremental Subspace Learning Algorithm to Categorize Large Scale Text Data
The dramatic growth in the number and size of on-line information sources has fueled increasing research interest in the incremental subspace learning problem. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Inter-class Scatter (IIS) algorithm. Unlike traditional batch learners, IIS learns from a stream of training data, not a set. IIS overcomes the inherent problem of some other incremental operations such as Incremental Principal Component Analysis (PCA) and Incremental Linear Discriminant Analysis (LDA). The experimental results on the synthetic datasets show that IIS performs as well as LDA and is more robust against noise. In addition, the experiments on the Reuters Corpus Volume 1 (RCV1) dataset show that IIS outperforms state-of-the-art Incremental Principal Component Analysis (IPCA) algorithm, a related algorithm, and Information Gain in efficiency and effectiveness respectively.
5296157
A Novel Scalable Algorithm for Supervised Subspace Learning
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as supervised Kampong measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, information gain (IG).
8600912
Performance Evaluation of Distributed Web Server Architectures under E-Commerce Workloads
One of the central and key components to make the ECommerce a success is high performance and highly reliable web server architectures. Extensive research has been sparked aiming at improving web server performance. Fundamental to the goal of improving web server performance is a solid understanding of behavior and performance of web servers. However, very little research is found on evaluating web server performance based on realistic workload representing E-Commerce applications which are usually exemplified by a large amount of CGI (Common Gateway Interface), ASP (Active Server Page), or Servlet (Java Server-side interface) calls. This paper presents a performance study under the workload with a mixture of static web page requests, CGI requests, Servlet requests, and database queries. System throughputs and user response times are measured for five different server architectures consisting of PCs that run both a web server program and a database. We observed that performance behaviors of the web server architectures considered under this mixed workload are quite different from that under static page workload and sometimes counter-intuitive. Our performance results suggest that there is a large room for potential performance improvement for web servers.
1414068
Theory and Algorithms for Plan Merging
Abstract Merging operators in a plan can yield significant savings in the cost to execute a plan. This paper provides a formal theory for plan merging and presents both optimal and efficient heuristic algorithms for finding minimum cost merged plans. The optimal plan merging algorithm applies a dynamic programming method to handle multiple linear plans and is extended to partially ordered plans in a novel way. Furthermore, with worst-case and average-case complexity analysis and empirical tests, we demonstrate that efficient and well-behaved approximation algorithms are applicable for optimizing plans with large sizes.