FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3024299164> ?p ?o ?g. }

• W3024299164 abstract "Signal classification is widely applied in science and engineering such as in audio and visual signal processing. The performance of a typical classification system depends highly on the features (used to represent a signal in a lower dimensional space) and the classification algorithms (used to determine the category of the signal based on the features). Recent developments show that dictionary learning based sparse representation techniques have the potential to offer improved performance over the conventional techniques for feature extraction, such as mel frequency cepstrum coefficient (MFCC) and classifier design, such as support vector machine (SVM). In this thesis, we focus on dictionary learning based methods for signal classification and address several challenges as explained below. First we study the potential of using dictionary learning algorithms such as K-SVD for sparse feature extraction obtained by Orthogonal Matching Pursuit (OMP). Specifically, we have proposed the use of pooling and sampling techniques in audio domain to unify the dimension of feature vectors, and to improve computational efficiency. The proposed algorithm is also shown to have advantages for noisy signal classification. Most dictionary learning algorithms have been developed for vector/matrix form of data. Our second contribution is to extend dictionary learning algorithms for high dimensional tensor data and use them to design classifiers. Different from existing tensor dictionary learning methods, we introduce various constraints on the dictionary learning process such as structured sparsity constraints on the core tensor and discriminative constraints on the dictionaries based on the data-spread information measured by Fisher criterion. Such constraints facilitate the design of discriminative classifiers based on reconstruction error and further improve the overall performance even with reduced amount of training data. Recently, structured block sparsity in vector/matrix based dictionary learning method has been shown to outperform signal classification in terms of non-block sparse reconstruction error. In our third contribution, we extend the concept of structured-block sparsity to tensors by providing underlying dictionaries with block structure. We develop an algorithm for structured block-sparse tensor representation and perform classification based upon the block sparse tensor reconstruction error. Our algorithm shows improved performance over its matrix based counter-parts and comparable performance with our previous tensor based method. Our dictionary learning based classification methods are applied on audio and image data for various application scenarios such as speech and music discrimination, speaker identification, digit and face recognition. The experimental results confirm the advantage of the proposed algorithms over several state-of-the-art baseline algorithms." @default.
• W3024299164 created "2020-05-21" @default.
• W3024299164 creator A5043995656 @default.
• W3024299164 date "2014-01-01" @default.
• W3024299164 modified "2023-09-27" @default.
• W3024299164 title "Dictionary Learning for Signal Classification." @default.
• W3024299164 hasPublicationYear "2014" @default.
• W3024299164 type Work @default.
• W3024299164 sameAs 3024299164 @default.
• W3024299164 citedByCount "0" @default.
• W3024299164 crossrefType "dissertation" @default.
• W3024299164 hasAuthorship W3024299164A5043995656 @default.
• W3024299164 hasConcept C104267543 @default.
• W3024299164 hasConcept C119857082 @default.
• W3024299164 hasConcept C12267149 @default.
• W3024299164 hasConcept C124066611 @default.
• W3024299164 hasConcept C124851039 @default.
• W3024299164 hasConcept C151989614 @default.
• W3024299164 hasConcept C153180895 @default.
• W3024299164 hasConcept C154771677 @default.
• W3024299164 hasConcept C154945302 @default.
• W3024299164 hasConcept C156872377 @default.
• W3024299164 hasConcept C28490314 @default.
• W3024299164 hasConcept C41008148 @default.
• W3024299164 hasConcept C52622490 @default.
• W3024299164 hasConcept C70437156 @default.
• W3024299164 hasConcept C70518039 @default.
• W3024299164 hasConcept C83665646 @default.
• W3024299164 hasConcept C84462506 @default.
• W3024299164 hasConcept C9390403 @default.
• W3024299164 hasConcept C95623464 @default.
• W3024299164 hasConcept C97931131 @default.
• W3024299164 hasConceptScore W3024299164C104267543 @default.
• W3024299164 hasConceptScore W3024299164C119857082 @default.
• W3024299164 hasConceptScore W3024299164C12267149 @default.
• W3024299164 hasConceptScore W3024299164C124066611 @default.
• W3024299164 hasConceptScore W3024299164C124851039 @default.
• W3024299164 hasConceptScore W3024299164C151989614 @default.
• W3024299164 hasConceptScore W3024299164C153180895 @default.
• W3024299164 hasConceptScore W3024299164C154771677 @default.
• W3024299164 hasConceptScore W3024299164C154945302 @default.
• W3024299164 hasConceptScore W3024299164C156872377 @default.
• W3024299164 hasConceptScore W3024299164C28490314 @default.
• W3024299164 hasConceptScore W3024299164C41008148 @default.
• W3024299164 hasConceptScore W3024299164C52622490 @default.
• W3024299164 hasConceptScore W3024299164C70437156 @default.
• W3024299164 hasConceptScore W3024299164C70518039 @default.
• W3024299164 hasConceptScore W3024299164C83665646 @default.
• W3024299164 hasConceptScore W3024299164C84462506 @default.
• W3024299164 hasConceptScore W3024299164C9390403 @default.
• W3024299164 hasConceptScore W3024299164C95623464 @default.
• W3024299164 hasConceptScore W3024299164C97931131 @default.
• W3024299164 hasLocation W30242991641 @default.
• W3024299164 hasOpenAccess W3024299164 @default.
• W3024299164 hasPrimaryLocation W30242991641 @default.
• W3024299164 hasRelatedWork W1530199860 @default.
• W3024299164 hasRelatedWork W1568165162 @default.
• W3024299164 hasRelatedWork W1923938124 @default.
• W3024299164 hasRelatedWork W2024378471 @default.
• W3024299164 hasRelatedWork W2064227837 @default.
• W3024299164 hasRelatedWork W2243723318 @default.
• W3024299164 hasRelatedWork W2490391915 @default.
• W3024299164 hasRelatedWork W2509533530 @default.
• W3024299164 hasRelatedWork W2573108165 @default.
• W3024299164 hasRelatedWork W2766615582 @default.
• W3024299164 hasRelatedWork W2786257000 @default.
• W3024299164 hasRelatedWork W2927534732 @default.
• W3024299164 hasRelatedWork W2966529942 @default.
• W3024299164 hasRelatedWork W2966714923 @default.
• W3024299164 hasRelatedWork W3002335951 @default.
• W3024299164 hasRelatedWork W3035748890 @default.
• W3024299164 hasRelatedWork W3099014258 @default.
• W3024299164 hasRelatedWork W3122791082 @default.
• W3024299164 hasRelatedWork W3139466287 @default.
• W3024299164 hasRelatedWork W3212841649 @default.
• W3024299164 isParatext "false" @default.
• W3024299164 isRetracted "false" @default.
• W3024299164 magId "3024299164" @default.
• W3024299164 workType "dissertation" @default.