11821573
Phase Transition of Tractability in Constraint Satisfaction and Bayesian Network Inference
There has been great interest in identifying tractable subclasses of NP complete problems and designing efficient algorithms for these tractable classes. Constraint satisfaction and Bayesian network inference are two examples of such problems that are of great importance in AI and algorithms. In this paper we study, under the frameworks of random constraint satisfaction problems and random Bayesian networks, a typical tractable subclass characterized by the treewidth of the problems. We show that the property of having a bounded treewidth for CSPs and Bayesian network inference problem has a phase transition that occurs while the underlying structures of problems are still sparse. This implies that algorithms making use of treewidth based structural knowledge only work efficiently in a limited range of random instance.
3221081
Consistency and Random Constraint Satisfaction Models
In this paper, we study the possibility of designing non-trivial random CSP models by exploiting the intrinsic connection between structures and typical-case hardness. We show that constraint consistency, a notion that has been developed to improve the efficiency of CSP algorithms, is in fact the key to the design of random CSP models that have interesting phase transition behavior and guaranteed exponential resolution complexity without putting much restriction on the parameter of constraint tightness or the domain size of the problem. We propose a very flexible framework for constructing problem instances with interesting behavior and develop a variety of concrete methods to construct specific random CSP models that enforce different levels of constraint consistency. 
 
A series of experimental studies with interesting observations are carried out to illustrate the effectiveness of introducing structural elements in random instances, to verify the robustness of our proposal, and to investigate features of some specific models based on our framework that are highly related to the behavior of backtracking search algorithms.
2287582
Resolution complexity of random constraint satisfaction problems: Another half of the story
Let C"n","m^2^,^k^,^t be a random constraint satisfaction problem (CSP) on n binary variables, where m constraints are selected uniformly at random from all the possible k-ary constraints each of which contains exactly t tuples of the values as its restrictions. We establish an upper bound on the constraint tightness threshold for C"n","m^2^,^k^,^t to have an exponential resolution complexity. The upper bound partly answers the open problem regarding the CSP resolution complexity with the tightness between the existing upper and lower bounds [D. Mitchell, Resolution complexity of random constraints, in: Proceedings Principles and Practices of Constraint Programming-CP 2002, Springer, Berlin, 2002, pp. 295-309].
13799231
Improving molecular cancer class discovery through sparse non-negative matrix factorization
MOTIVATION
Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process.


RESULTS
We report an improved unsupervised method for cancer classification by the use of gene-expression profile via sparse non-negative matrix factorization. We demonstrate the improvement by direct comparison with classic non-negative matrix factorization on the three well-studied datasets. In addition, we illustrate how to identify a small subset of co-expressed genes that may be directly involved in cancer.
10844784
Meta-game Equilibrium for Multi-agent Reinforcement Learning
This paper proposes a multi-agent Q-learning algorithm called meta-game-Q learning that is developed from the meta-game equilibrium concept Different from Nash equilibrium, meta-game equilibrium can achieve the optimal joint action game through deliberating its preference and predicting others' policies in the general-sum game A distributed negotiation algorithm is used to solve the meta-game equilibrium problem instead of using centralized linear programming algorithms We use the repeated prisoner's dilemma example to empirically demonstrate that the algorithm converges to meta-game equilibrium.
16621894
Feature-level fusion in personal identification
The existing studies of multi-modal and multi-view personal identification focused on combining the outputs of multiple classifiers at the decision level. In this study, we investigated the fusion at the feature level to combine multiple views and modals in personal identification. A new similarity measure is proposed, which integrates multiple 2D view features representing a visual identity of a 3D object seen from different viewpoints and from different sensors. The robustness to non-rigid distortions is achieved by the proximity correspondence manner in the similarity computation. The feasibility and capability of the proposed technique for personal identification were evaluated on multiple view human faces and palmprints. This research demonstrates that the feature-level fusion provides a new way to combine multiple modals and views for personal identification.
975623
Face Recognition by Combining Kernel Associative Memory and Gabor Transforms
Kernel associative memory (KAM) has previously been proposed as an efficient scheme for face recognition. In this paper, a hybrid method of combining KAM and Gabor wavelet transform is proposed. In this method, face images of each person are first decomposed into their spatial/frequency domains by Gabor transforms, which are then modelled by a KAM. While Gabor properties of orientation selectivity and spatial frequency selectivity provide discriminating features, KAM offers the means to capture the important intra-class variations. Experimental results obtained on two standard face databases demonstrated that the proposed method consistently improved the system performance
17160551
Pattern discovery on character sets and real-valued data: linear bound on irredundant motifs and an efficient polynomial time algorithm
Given an input sequence of data, a motif is a repeating pattern, possibly interspersed with "dont care" characters. The data could be a sequence of characters or sets of characters or even real values. In the first two cases, the number of motifs could potentially be exponential in the size of the input sequence and in the third case there could be infinite number of motifs (assuming two real numbers are equal if they are within some specified 5 > 0 of each other). However, it has been shown in some motif based applications, such as multiple sequence aligz~nent [Par98, PFR99], that a small subset of these motifs capture all the relevant information. Also, from an information-theoretic viewpoint, the special motifs represent the sets with low co-relation, hence of higher informational value. In this paper, we show, for any sequence with n characters, there exists only a linear (or no more than 3n) number of these special motifs and every other motif can be constructed from this set. We term these special motifs as irredundant motifs. We also present an efficient polynomial time algorithm to generate these motifs. This bound on the number of useful motifs ~ves validation to motif-based approaches, since the total number of irredundant motifs does not explode. This result is potentially of significance to most applications that use pattern discovery as the basic engine such as data mining, clustering, matching and others.