2,585 research outputs found
Localized Regression
The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization is combined with a reduction of dimension. In particular it is shown that localization yields powerful classifiers even in higher dimensions if localization is combined with locally adaptive selection of predictors. A robust localized logistic regression (LLR) method is developed for which all tuning parameters are chosen dataÂĄadaptively. In an extended simulation study we evaluate the potential of the proposed procedure for various types of data and compare it to other classification procedures. In addition we demonstrate that automatic choice of localization, predictor selection and penalty parameters based on cross validation is working well. Finally the method is applied to real data sets and its real world performance is compared to alternative procedures
Non-uniform Feature Sampling for Decision Tree Ensembles
We study the effectiveness of non-uniform randomized feature selection in
decision tree classification. We experimentally evaluate two feature selection
methodologies, based on information extracted from the provided dataset:
\emph{leverage scores-based} and \emph{norm-based} feature selection.
Experimental evaluation of the proposed feature selection techniques indicate
that such approaches might be more effective compared to naive uniform feature
selection and moreover having comparable performance to the random forest
algorithm [3]Comment: 7 pages, 7 figures, 1 tabl
Full-info Training for Deep Speaker Feature Learning
In recent studies, it has shown that speaker patterns can be learned from
very short speech segments (e.g., 0.3 seconds) by a carefully designed
convolutional & time-delay deep neural network (CT-DNN) model. By enforcing the
model to discriminate the speakers in the training data, frame-level speaker
features can be derived from the last hidden layer. In spite of its good
performance, a potential problem of the present model is that it involves a
parametric classifier, i.e., the last affine layer, which may consume some
discriminative knowledge, thus leading to `information leak' for the feature
learning. This paper presents a full-info training approach that discards the
parametric classifier and enforces all the discriminative knowledge learned by
the feature net. Our experiments on the Fisher database demonstrate that this
new training scheme can produce more coherent features, leading to consistent
and notable performance improvement on the speaker verification task.Comment: Accepted by ICASSP 201
Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data
In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrapand k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead tocorrect classification rates with less than 10% of the original features
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