Motivated by the problem of nding light (i.e., low weight) and short (i.e., low degree) codewords in narrow-sense, primitive BCH codes we consider the problem of determining the probability that a random t-set of vectors in an n dimensional vector space over GF (q) contains an r-dimensional subspace (or aane subspace). We nd some bounds for this and similar probabilities and apply these techniq...

Classifier fusion strategies have shown great potential to enhance the performance of pattern recognition systems. There is an agreement among researchers in classifier combination that the major factor for producing better accuracy is the diversity in the classifier team. Re-sampling based approaches like bagging, boosting and random subspace generate multiple models by training a single learn...

Predicting potential advertisement clicks of users are important for advertisement recommendation, advertisement placement, presentation pricing, and so on. In this paper, several machine learning algorithms including conditional random fields (CRF), support vector machines (SVM), decision tree (DT) and backpropagation neural networks (BPN) are developed to learn user’s click behaviors from adv...

Relevance feedback has been an indispensable component for multimedia retrieval systems. In this paper, we present an adaptive pattern discovery method, which addresses relevance feedback by interactively discovering meaningful patterns of relevant objects. To facilitate pattern discovery, we first present a dynamic feature extraction method, which aims to alleviate the curse of dimensionality ...

We extend the Gaussian scale mixture model of dependent subspace source densities to include non-radially symmetric densities using Generalized Gaussian random variables linked by a common variance. We also introduce the modeling of skew in source densities and subspaces using a generalization of the Normal Variance-Mean mixture model. We give closed form expressions for subspace likelihoods an...

Compressive sensing has been rapidly growing as a non-adaptive dimensionality reduction framework, wherein high-dimensional data is projected onto a randomly generated subspace. In this paper we explore a paradigm called compressive reinforcement learning, where approximately optimal policies are computed in a lowdimensional subspace generated from a high-dimensional feature space through rando...

We present a framework for supervised subspace tracking, when there are two time series xt and yt, one being the high-dimensional predictors and the other being the response variables and the subspace tracking needs to take into consideration of both sequences. It extends the classic online subspace tracking work which can be viewed as tracking of xt only. Our online sufficient dimensionality r...

Subspace methods are essential to high-resolution environment sensing in the emerging unmanned systems, if further combined with millimeter-wave (mm-Wave) massive multi-input multi-output (MIMO) technique. The estimation of signal/noise subspace, as one critical step, is yet computationally complex and presents a particular challenge when developing low-complexity automotive radars. In this wor...

The construction of codes in the projective space for error control in random networks has been the focus of recent research. The Plücker coordinate description of subspaces has been discussed in the context of constant dimension codes, as well as the Schubert cell description of certain code parameters. In this paper we use this classical tool to reformulate some standard constructions of cons...

Subspace codes are known to be useful in errorcorrection for random network coding. Recently, they were used to prove that vector network codes outperform scalar linear network codes, on multicast networks, with respect to the alphabet size. In both cases, the subspace distance is used as the distance measure. In this work we show that we can replace the subspace distance with two other possibl...