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In this paper, we develop the max-margin similarity preserving factor analysis (MMSPFA) model. MMSPFA utilizes the latent variable support vector machine (LVSVM) as the classification criterion in the latent space to learn a discriminative subspace with max-margin constraint. It jointly learns factor analysis (FA) model, similarity preserving (SP) term and max-margin classifier in a united Bayesian...
Cognitive radio devices opportunistically operate on whitespace channels, provided those channels are not in use by the primary users. This opportunistic reusing of channels requires secondary users to perform fast and efficient sensing to determine the unused channels. Although individual secondary clients may be unwilling to frequently sense all the channels, their density could be exploited for...
As a new information acquisition and processing technology, wireless sensor networks can achieve tasks with complex large-scale monitoring and tracking in wide range of applications, but the localization for node itself is the basis of most applications. APIT is a major localization algorithm of ranged free algorithms, but the algorithm has the problems of big localization errors and low coverage...
Feature selection has been a key technique in massive data processing, e.g. microarray data analysis with few samples but high dimensions. One common problem in multi-class data analysis is the unbalanced recognition accuracies among classes, which leads to poor system performance. One main reason is that most feature selection methods focus on the performance of whole dataset while pay little attention...
This paper used mixed logit model to predict credit risk of listed companies in China. In order to reduce the difficulty in dealing with the facts of correlation and multidimension of the financial indexes of listed companies and meanwhile to ensure that the data are not lost, we introduced factor analysis to the mixed logit equation and constructed a factor analysis mixed logit model. Fifteen factors...
This paper proposes a self-constructed Mercer kernel based subspace LDA approach for face recognition. Our self-constructed Mercer (SM) kernel function is constructed from a given block diagonal matrix. The entries of all its block diagonal sub-matrices are equal to 1. It shows that this kind of matrix is a symmetric, positive semi-definite matrix and thus can serve as a kernel matrix. Based on such...
This paper is to introduce a novel semi-supervised learning algorithm named linear neighborhood spread (LNS), which is capable for learning manifold structures. Labeled and unlabeled data are represented as vertices in a weighted graph, and each data point is assumed can be linearly constructed from its neighborhood. Labels are spread through the edges, and the weighted graph is regarded as probabilistic...
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