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Sparse output coding for Scalable Visual Recognition

INTERNATIONAL JOURNAL OF COMPUTER VISION 2016年第1期119卷 60-75页

作者： Zhao, Bin Xing, Eric P. Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach.

关键词： Scalable classification output coding Probabilistic decoding Object recognition Scene recognition

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N-division output coding method applied to face recognition

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PATTERN RECOGNITION LETTERS 2003年第16期24卷 3115-3123页

作者： Ko, J Byun, H Yonsei Univ Coll Engn Dept Comp Sci Sudaernoon Ku Seoul 120749 South Korea

Most research on face recognition has focused on representation of face appearances rather than the classifiers. For robust classification performance, we need to adopt elaborate classifiers. output coding is suitable for this purpose because it can allow online learning. In this paper, we propose an N-division output coding method. In the experiments we demonstrate such properties as problem complexity, margin of separation, machine relevance and the recognition performance among different output coding methods. (C) 2003 Elsevier B.V. All rights reserved.

关键词： face recognition output coding support vector machines

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Reducing multiclass cancer classification to binary by output coding and SVM

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2006年第1期30卷 63-71页

作者： Shen, L Tan, EC Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance. (C) 2005 Elsevier Ltd. All rights reserved.

关键词： multiclass cancer classification microarrays output coding support vector machine

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Discriminative Feature Transformation using output coding for Speech Recognition

Discriminative Feature Transformation using Output Coding fo...

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10th INTERSPEECH 2009 Conference

作者： Dehzangi, Omid Ma, Bin Chng, Eng Siong Li, Haizhou Nanyang Technol Univ Sch Comp Engn Singapore Singapore Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781615676927

In this paper, we present a new mechanism to extract discriminative acoustic features for speech recognition using continuous output coding (COC) based feature transformation. Our proposed method first expands the short-time spectral features into a higher dimensional feature space to improve its discriminative capability. The expansion is performed by employing the polynomial expansion. The high dimension features are then projected into lower dimension space using continuous output coding technique implemented by a set of linear SVMs. The resulting feature vectors are designed to encode the difference between phones. The generated features are shown to be more discriminative than MFCCs and experimental results on both TIMIT and NTIMIT corpus showed better phone recognition accuracy with the proposed features.

关键词： speech recognition discriminative features polynomial expansion output coding SVM

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DISCRIMINATIVE output coding FEATURES FOR SPEECH RECOGNITION

DISCRIMINATIVE OUTPUT CODING FEATURES FOR SPEECH RECOGNITION

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6th International Symposium on Chinese Spoken Language Processing

作者： Dehzangi, Omid Ma, Bin Chng, Eng Siong Li, Haizhou Nanyang Technol Univ Sch Comp Engn Singapore Singapore Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781424429424

This paper presents a novel approach of discriminative acoustic feature extraction for speech recognition using output coding technique. A high dimensional feature space for higher discriminative capability is constructed by expanding MFCC coefficients with polynomial expansion. In order to fit the discriminative features in the hidden Markov model structure of speech recognition, the high dimensional feature vectors are further projected into a low dimensional feature space using the output scores of a set of SVMs. Each of the SVMs is trained in one phone versus the rest manner so that each of the resulting feature dimensions can provide effective information to differ one phone from the others. The discriminative features have been evaluated in the speech recognition task of the TIMIT corpus, and 72.18% phone accuracy has been achieved.

关键词： speech recognition discriminative features output coding polynomial expansion SVM

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An output-coding-Based Detection Scheme Against Replay Attacks in Cyber-Physical Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第10期68卷 3306-3310页

作者： Guo, Haibin Pang, Zhong-Hua Sun, Jian Li, Jun Beijing Inst Technol Sch Automat State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China North China Univ Technol Key Lab Fieldbus Technol & Automat Beijing Beijing 100144 Peoples R China China Ind Control Syst Cyber Emergency Response T Beijing 100040 Peoples R China

This brief studies the detection problem of replay attacks against measurement data transmitted over the feedback channel of a cyber-physical system. In order to detect such replay attacks, an output coding scheme, where the control input is coded into the measurement output transmitted in the feedback channel, is proposed to make the output residuals between the normal and compromised cases significantly different, which thus triggers an alarm from an anomaly detector. The main advantage of this scheme is that the system performance in the normal situation is not affected compared with existing coding detection schemes, which is also the practical motivation of our work. In addition, the encoded measurement output is directly used to estimate the system state instead of setting a decoder. Both theoretical analysis and simulation results are provided to demonstrate the effectiveness of the proposed scheme.

关键词： Encoding Watermarking Kalman filters Noise measurement Detectors Covariance matrices System performance Cyber-physical systems (CPSs) replay attacks attack detection output coding

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Recursive ECOC classification

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PATTERN RECOGNITION LETTERS 2010年第3期31卷 210-215页

作者： Tapia, E. Bulacio, P. Angelone, L. Consejo Nacl Invest Cient & Tecn Cifasis Rosario Santa Fe Argentina Natl Univ Rosario Fac Cs Exactas & Ingn Rosario Santa Fe Argentina

The construction of ECOC (Error Correcting output coding) classifiers from one or more constituent ECOC classifiers is proposed. Aiming to boost the accuracy of the overall ECOC system, constituent ECOC classifiers are allowed to exchange information via shared binary classifiers. A novel decoding algorithm that iteratively combines binary predictions from constituent ECOC classifiers is introduced for this purpose. Aiming to minimize the degrading effects of dependency between binary predictions, the use of sparsely connected ECOC classifiers of small size is recommended. A comprehensive experimental work shows that competitive ECOC classifiers of size at most inverted right perpendicular3 . log(2)Minverted left perpendicular can be obtained in this way. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Multiclass classification output coding ECOC

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On the learnability and design of output codes for multiclass problems

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MACHINE LEARNING 2002年第2-3期47卷 201-233页

作者： Crammer, K Singer, Y Hebrew Univ Jerusalem Sch Engn & Comp Sci IL-91904 Jerusalem Israel

output coding is a general framework for solving multiclass categorization problems. Previous research on output codes has focused on building multiclass machines given predefined output codes. In this paper we discuss for the first time the problem of designing output codes for multiclass problems. For the design problem of discrete codes, which have been used extensively in previous works, we present mostly negative results. We then introduce the notion of continuous codes and cast the design problem of continuous codes as a constrained optimization problem. We describe three optimization problems corresponding to three different norms of the code matrix. Interestingly, for the l(2) norm our formalism results in a quadratic program whose dual does not depend on the length of the code. A special case of our formalism provides a multiclass scheme for building support vector machines which can be solved efficiently. We give a time and space efficient algorithm for solving the quadratic program. We describe preliminary experiments with synthetic data show that our algorithm is often two orders of magnitude faster than standard quadratic programming packages. We conclude with the generalization properties of the algorithm.

关键词： multiclass categorization output coding SVM

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An empirical study of binary classifier fusion methods for multiclass classification

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INFORMATION FUSION 2011年第2期12卷 111-130页

作者： Garcia-Pedrajas, Nicolas Ortiz-Boyer, Domingo Univ Cordoba Dept Comp & Numer Anal E-14071 Cordoba Spain

One of the most important topics in information fusion is the combination of individual classifiers in multi-classifier systems. We have two different tasks in this area: one is the training and construction of ensembles of classifiers, with each one being able to solve the multiclass problem;the other task is the fusion of binary classifiers, with each one solving a different two-class problem to construct a multiclass classifier. This paper is devoted to the study of several aspects on the fusion process of binary classifiers to obtain a multiclass classifier. In the general case of a classification problem with more than two classes, we are faced with the issue that many algorithms either work better with two-class problems or are specifically designed for two-class problems. In such cases, a binarization method that maps the multiclass problem into several two-class problems must be used. In this task, information fusion plays a central role because of the combination of the prediction of the different binary classifiers into a multiclass classifier. Several issues regarding the way binary learners are trained and combined are raised by this task. Issues such as individual accuracy, diversity, and independence are common to other information fusion tasks such as the construction of ensembles of classifiers. This paper presents a study of the different class binarization methods for the various standard multiclass classification problems that have been proposed while addressing aspects not considered in previous works. We are especially concerned with many of the general assumptions in the field that have not been fully assessed by experimentation. We test the different methods in a large set of real-world problems from the UCI Machine Learning Repository, and we use six different base learners. Our results corroborate some of the previous results present in the literature. Furthermore, we present new results regarding the influence of the base learner on the per

关键词： Multiclass problems Class binarization output coding Classification

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Discriminative feature extraction for speech recognition using continuous output codes

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PATTERN RECOGNITION LETTERS 2012年第13期33卷 1703-1709页

作者： Dehzangi, Omid Ma, Bin Chng, Eng Siong Li, Haizhou Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Inst Infocomm Res Singapore 138632 Singapore Univ Eastern Finland FI-80101 Joensuu Finland

Feature transformation techniques have been widely investigated to reduce feature redundancy and to introduce additional discriminative information with the aim to improve the performance of automatic speech recognition (ASR). In this paper, we propose a novel method to obtain discriminative feature transformation based on output coding technique for speech recognition. The output coding transformation projects the speech features from their original space to a new one where each dimension of the features captures information to distinguish different phones. Using polynomial expansion, the short-time spectral features are first expanded to a high-dimensional space where the generalized linear discriminant sequence kernel is applied on the sequences of input feature vectors. Then, the output coding transformation formulated via a set of linear SVMs projects the sequences of high dimensional vectors into a tractable low-dimensional feature space where the resultant features are well-separated continuous output codes for the subsequent multi-class classification problem. Our experimental results on the TIMIT corpus show that the proposed features achieve 10.5% ASR error rate reduction over the conventional spectral features. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Speech recognition Feature transformation Generalized discriminant analysis output coding

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