Missing data is one of the most common issues encountered in cleaning process especially when dealing with medical dataset. A real collected dataset prone to be incomplete, inconsistent, noisy and redundant due potential reasons such as human errors, instrumental failures, adverse death. Therefore, accurately deal incomplete data, a sophisticated algorithm proposed impute those missing values. ...

Typical address-oriented computer memories cannot recognize incomplete or noisy information. Associative (content-addressable) memories solve this problem but suffer from severe capacity shortages. I propose a model of a quantum memory that solves both problems. The storage capacity is exponential in the number of qbits and thus optimal. The retrieval mechanism for incomplete or noisy inputs is...

We study the problem of recovering a smooth graph signal from incomplete noisy measurements, using random sampling to choose from a subset of graph nodes. The signal recovery is formulated as a convex optimization problem. The optimality conditions form a system of linear equations which is solvable via Laplacian solvers. In particular, we use an incomplete Cholesky factorization conjugate grad...

Noisy and incomplete data restoration is a critical preprocessing step in developing effective learning algorithms, which targets to reduce the effect of noise and missing values in data. By utilizing attribute correlations and/or instance similarities, various techniques have been developed for data denoising and imputation tasks. However, current existing data restoration methods are either s...

An overcomplete logic is a logic that ‘ceases to make the difference’: According to such a logic, all inferences hold independently of the nature of the statements involved. A negation-inconsistent logic is a logic having at least one model that satisfies both some statement and its negation. A negation-incomplete logic has at least one model according to which neither some statement nor its ne...

Sensor networks have shown tremendous growth in many domains such as environmental monitoring. The data captured from the physical world through these sensor devices, however, tend to be incomplete, noisy, and unreliable. Traditional data cleaning techniques cannot be applied to such data as they do not take into account the strong spatial and temporal correlations typically present in sensor d...

—In this paper we are going to estimate the sum capacity of a binary CDMA system in presence of the near-far effect. We model the near-far effect as a random variable that is multiplied by the users binary data before entering the noisy channel. We will find a lower bound and a conjectured upper bound for the sum capacity in this situation. All the derivations are in the asymptotic case. Simula...

Incremental on-line learning is a research topic gaining increasing interest in the machine learning community. Such learning methods are highly adaptive, not restricted to distinct training and application phases, and applicable to large volumes of data. In this paper, we present a novel classifier based on the unsupervised topologylearning TopoART neural network. We demonstrate that this clas...

This paper proposes a general framework for compressed sensing of constrained joint sparsity (CJS) which includes total variation minimization (TV-min) as an example. The gradientand 2-norm error bounds, independent of the ambient dimension, are derived for the CJS version of Basis Pursuit and Orthogonal Matching Pursuit. As an application the results extend Candès, Romberg and Tao’s proof of e...

In recent years, because CCD cameras have become inexpensive and ever more prevalent, there has been increasing interest in modelling human shape and motion from acquired image data. Such ability has many applications, such as entertainment, sports medicine/athletic training, and biometry. This, however, is an inherently difficult task, both because the body is very complex and because the data...