16679508
Regularized Neighborhood Component Analysis
Discriminative feature extraction is one of the fundamental problems in pattern recognition and signal processing. It was recently proposed that maximizing the class prediction by neighboring samples in the transformed space is an effective objective for learning a low-dimensional linear embedding of labeled data. The associated methods, Neighborhood Component Analysis (NCA) and Relevant Component Analysis (RCA), have been proven to be useful preprocessing techniques for discriminative information visualization and classification. We point out here that NCA and RCA are prone to overfitting and therefore regularization is required. NCA and RCA's failure for high-dimensional data is demonstrated in this paper by experiments in facial image processing. We also propose to incorporate a Gaussian prior into the NCA objective and obtain the Regularized Neighborhood Component Analysis (RNCA). The empirical results show that the generalization can be significantly enhanced by using the proposed regularization method.
6993641
A Fast Fixed-Point Algorithm for Two-Class Discriminative Feature Extraction
We propose a fast fixed-point algorithm to improve the Relevant Component Analysis (RCA) in two-class cases. Using an objective function that maximizes the predictive information, our method is able to extract more than one discriminative component of data for two-class problems, which cannot be accomplished by classical Fisher's discriminant analysis. After prewhitening the data, we apply Newton's optimization method which automatically chooses the learning rate in the iterative training of each component. The convergence of the iterative learning is quadratic, i.e. much faster than the linear optimization by gradient. methods. Empirical tests presented in the paper show that feature extraction using the new method resembles RCA for low-dimensional ionosphere data and significantly outperforms the latter in efficiency for high-dimensional facial image data.
15396672
Maintaining System State Information in a Multiagent Environment for Effective Learning
One fundamental issue in multiagent reinforcement learning is how to deal with the limited local knowledge of an agent in order to achieve effective learning. In this paper, we argue that this issue can be more effectively solved if agents are equipped with a consistent global view. We achieve this by requiring agents to follow an interacting protocol. The properties of the protocol are derived and theoretically analyzed. A distributed protocol that satisfies these properties is presented. The experimental evaluations are conducted for a well-known test-case (i.e., pursuit game) in the context of two learning algorithms. The results show that the protocol is effective and the reinforcement learning algorithms using it perform much better. key words: multiagent system, system state, distributed protocol, token ring
586755
Mixed symbolic representations for model checking software programs
We present an efficient symbolic search algorithm for software model checking. The algorithm combines multiple symbolic representations to efficiently represent the transition relation and reachable states and uses a combination of decision procedures for Boolean and integer representations. Our main contributions include: (1) mixed symbolic representations to model C programs with rich data types and complex expressions; and (2) new symbolic search strategies and optimization techniques specific to sequential programs that can significantly improve the scalability of model checking algorithms. Our controlled experiments on real-world software programs show that the new symbolic search algorithm can achieve several orders-of-magnitude improvements over existing methods. The proposed techniques are extremely competitive in handling sequential models of non-trivial sizes, and also compare favorably to popular Boolean-level model checking algorithms based on BDDs and SAT
11276211
PDC: Pattern discovery with confidence in DNA sequences
Pattern discovery in DNA sequences is one of the most challenging tasks in molecular biology and computer science. The main goal of pattern discovery in DNA sequences is to identify sequences of important biological function hidden in the huge amounts of genomic sequences. Several methods and techniques have been proposed and implemented in this field. However, in order to reduce computational time and complexity, most of them either focus on finding short DNA patterns or require explicit specification of pattern lengths in advance. Scientists need to find longer patterns without specifying pattern lengths in advance and still have good performance.In this paper, we propose a pattern discovery algorithm called Pattern Discovery with Confidence (PDC). Based on biological studies, we propose a new measurement system that can identify overrepresented patterns inside DNA sequences. Using this measurement, PDC algorithm can narrow the search space by checking dependency along the pattern, thus extending the pattern as long as possible without the need to restrict or specify the length of a pattern in advance. Experimental tests demonstrate that this approach can find long, interesting patterns within a reasonable computation time.
30128374
Variable Reuse for Efficient Image Computation
Image computation, that is, computing the set of states reachable from a given set in one step, is a crucial component in typical tools for BDD-based symbolic reachability analysis. It has been shown that the size of the intermediate BDDs during image computation can be dramatically reduced via conjunctive partitioning of the transition relation and ordering the conjuncts for facilitating early quantification. In this paper, we propose to enhance the effectiveness of these techniques by reusing the quantified variables. Given an ordered set of conjuncts, if the last conjunct that uses a variable u appears before the first conjunct that uses another variable v, then v can be renamed to u, assuming u will be quantified immediately after its last use. In general, multiple variables can share the same identifier so the BDD nodes that are inactive but not garbage collected may be activated. We give a polynomial-time algorithm for generating the optimum number of variables that are required for image computation and show how to modify the image computation accounting for variable reuse. The savings for image computation are demonstrated on ISCAS’89 and Texas’97 benchmark models.
17249203
Privacy-Preserving Queries on Encrypted Data
Data confidentiality is a major concern in database systems. Encryption is a useful tool for protecting the confidentiality of sensitive data. However, when data is encrypted, performing queries becomes more challenging. In this paper, we study efficient and provably secure methods for queries on encrypted data stored in an outsourced database that may be susceptible to compromise. Specifically, we show that, in our system, even if an intruder breaks into the database and observes some interactions between the database and its users, he only learns very little about the data stored in the database and the queries performed on the data. Our work consists of several components. First, we consider databases in which each attribute has a finite domain and give a basic solution for certain kinds of queries on such databases. Then, we present two enhanced solutions, one with a stronger security guarantee and the other with accelerated queries. In addition to providing proofs of our security guarantees, we provide empirical performance evaluations. Our experiments demonstrate that our solutions are fast on large-sized real data.