Many clustering methods highly depend on extracted features. In this paper, we propose a joint optimization framework in terms of both feature extraction and discriminative clustering. We utilize graph regularized sparse codes as the features, and formulate sparse coding as the constraint for clustering. Two cost functions are developed based on entropy-minimization and maximum-margin clusterin...

We study new formulas based on Lyapunov exponents for entropy, mutual information, and capacity of finite state discrete time Markov channels. We also develop a method for directly computing mutual information and entropy using continuous state space Markov chains. Our methods allow for arbitrary input processes and channel dynamics, provided both have finite memory. We show that the entropy ra...

Rényi entropy is an information-theoretic measure of randomness which is fundamental to several applications. Several estimators of Rényi entropy based on k-nearest neighbor (kNN) based distances have been proposed in literature. For d-dimensional densities f , the variance of these Rényi entropy estimators of f decay as O(M), whereM is the sample size drawn from f . On the other hand, the bias...

We apply a treatment simulation and optimization approach to develop decision support guidance for warfarin precision treatment plans. Simulation include the use of ∼1,500,000 clinical avatars (simulated patients) generated by an integrated data-driven and domain-knowledge based Bayesian Network Modeling approach. Subsequently, we simulate 30-day individual patient response to warfarin treatmen...

The medical image registration algorithm uses the mutual information measure function that has many local extremes. Therefore, we propose our medical image registration algorithm that combines generalized mutual information with PSO-Powell hybrid algorithm and uses the objective measure function based on Renyi entropy. The Renyi entropy can remove the local extremes. We use the particle swarm o...

The mathematical modeling of the clustering centers problem leads to a min-sum-min formulation which, has the significant characteristic of being strongly nondifferentiable. To overcome this difficulty, a new filled function method is proposed to find centers of clusters based on entropy technique. A completely differentiable non-convex optimization model for the clustering center problem is co...

An image segmentation technique based on maximum fuzzy entropy is applied for Magnetic Resonance (MR) brain images to detect a brain tumor is presented in this paper. The proposed method performs image segmentation based on adaptive thresholding of the input MR brain images. The MR brain image is classified into two Membership Function (MF), whose MFs of the fuzzy region are Z-function and S-fu...

For a wide range of entropy measures, easy calculation of equilibria is possible using a principle of Game Theoretical Equilibrium related to Jaynes Maximum Entropy Principle. This follows previous work of the author and relates to Naudts [1], [2], and, partly, Abe and Bagci [3].

Instantaneous detection and diagnosis of various faults and break-downs in industrial processes is required to reduce production losses and damage to equipments. A solved knowledge-based system and developed techniques within artificial intelligence principles are showing its capability in assisting process operators to detect and diagnose faults and anomalies in industrial processes, especiall...