31,250 research outputs found
A robust approach to model-based classification based on trimming and constraints
In a standard classification framework a set of trustworthy learning data are
employed to build a decision rule, with the final aim of classifying unlabelled
units belonging to the test set. Therefore, unreliable labelled observations,
namely outliers and data with incorrect labels, can strongly undermine the
classifier performance, especially if the training size is small. The present
work introduces a robust modification to the Model-Based Classification
framework, employing impartial trimming and constraints on the ratio between
the maximum and the minimum eigenvalue of the group scatter matrices. The
proposed method effectively handles noise presence in both response and
exploratory variables, providing reliable classification even when dealing with
contaminated datasets. A robust information criterion is proposed for model
selection. Experiments on real and simulated data, artificially adulterated,
are provided to underline the benefits of the proposed method
A robust adaptive wavelet-based method for classification of meningioma histology images
Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
There are a number of studies about extraction of bottleneck (BN) features
from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases
and triphone states for improving the performance of text-dependent speaker
verification (TD-SV). However, a moderate success has been achieved. A recent
study [1] presented a time contrastive learning (TCL) concept to explore the
non-stationarity of brain signals for classification of brain states. Speech
signals have similar non-stationarity property, and TCL further has the
advantage of having no need for labeled data. We therefore present a TCL based
BN feature extraction method. The method uniformly partitions each speech
utterance in a training dataset into a predefined number of multi-frame
segments. Each segment in an utterance corresponds to one class, and class
labels are shared across utterances. DNNs are then trained to discriminate all
speech frames among the classes to exploit the temporal structure of speech. In
addition, we propose a segment-based unsupervised clustering algorithm to
re-assign class labels to the segments. TD-SV experiments were conducted on the
RedDots challenge database. The TCL-DNNs were trained using speech data of
fixed pass-phrases that were excluded from the TD-SV evaluation set, so the
learned features can be considered phrase-independent. We compare the
performance of the proposed TCL bottleneck (BN) feature with those of
short-time cepstral features and BN features extracted from DNNs discriminating
speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels
and boundaries are generated by three different automatic speech recognition
(ASR) systems. Experimental results show that the proposed TCL-BN outperforms
cepstral features and speaker+pass-phrase discriminant BN features, and its
performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted.
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