We propose a tracking algorithm based on image classification involving online feature weighting. The algorithm uses automatically produced general Haar-like features through feature extraction and feature selection using an online-built object model, and combines Principal Component Analysis (PCA, a generative method) and Fisher ́s Discriminative Analysis (FDA, a discriminative method). That is...

In this paper, application of sparse representation (factorization) of signals over an overcomplete basis (dictionary) for signal classification is discussed. Searching for the sparse representation of a signal over an overcomplete dictionary is achieved by optimizing an objective function that includes two terms: one that measures the signal reconstruction error and another that measures the s...

Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning...

Objective To develop early intelligent discriminative model of lung cancer and evaluate the efficiency of diagnosis value. Methods Based on the genetic polymorphism profile of CYP1A1-rs1048943, GSTM1, mEH-rs1051740, XRCC1-rs1799782 and XRCC1-rs25489 and the methylations of p16 and RASSF1A gene, and the length of telomere in the peripheral blood from 200 lung cancer patients and 200 health per...

Although many convex relaxations of clustering have been proposed in the past decade, current formulations remain restricted to spherical Gaussian or discriminative models and are susceptible to imbalanced clusters. To address these shortcomings, we propose a new class of convex relaxations that can be flexibly applied to more general forms of Bregman divergence clustering. By basing these new ...

The linear discriminant analysis (LDA) is one of the most traditional linear dimensionality reduction methods. This paper incorporates the inter-class relationships as relevance weights into the estimation of the overall within-class scatter matrix in order to improve the performance of the basic LDA method and some of its improved variants. We demonstrate that in some specific situations the s...

In the motivation of tapping the strong potential of computational intelligence in discovering knowledge of protective relay operations using data mining, modeling and simulation of an actual industrial numerical distance relay and its recording facility are a vital requisite. This is justified by the practicality and necessity of divulging the decision algorithm hidden in the recorded relay ev...

Concepts embody the knowledge to facilitate our cognitive processes of learning. Mapping short texts to a large set of open domain concepts has gained many successful applications. In this paper, we unify the existing conceptualization methods from a Bayesian perspective, and discuss the three modeling approaches: descriptive, generative, and discriminative models. Motivated by the discussion o...

Image annotation is an important open problem in computer vision. For this task we propose TagProp, a weighted nearest neighbor model, discriminatively trained to exploit labeled training images for predicting tags of test images. Neighbor weights are based on neighbor rank or distance. TagProp can learn the metric that defines neighbors by maximizing the log-likelihood of the tag predictions i...

Altered metabolism proceeding seroconversion in children progressing to Type 1 diabetes has previously been demonstrated. We tested the hypothesis that non-obese diabetic (NOD) mice show a similarly altered metabolic profile compared to C57BL/6 mice. Blood samples from NOD and C57BL/6 female mice was collected at 0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13 and 15 weeks and the metabolite content was anal...