• Journal of Advanced Navigation Technology
• /
• v.13 no.1
• /
• pp.151-155
• /
• 2009

We proposes a new method using the non-negative matrix factorization (NMF) and clustering method to extract the sentences for personalized document summarization. The proposed method uses clustering method for retrieving documents to extract sentences which are well reflected topics and sub-topics in document. Beside it can extract sentences with respect to query which are well reflected user interesting by using the inherent semantic features in document by NMF. The experimental results shows that the proposed method achieves better performance than other methods use the similarity and the NMF.

• The Journal of the Acoustical Society of Korea
• /
• v.34 no.3
• /
• pp.234-239
• /
• 2015

Non-Negative Matrix Factorization (NMF) is a method for updating dictionary and gain in alternating manner. Due to ease of implementation and intuitive interpretation, NMF is widely used to detect and separate overlapping sound events. However, NMF that utilizes non-negativity constraints generates parts-based representation and this distinct property leads to a dictionary containing fragmented acoustic events. As a result, the presence of shared basis results in performance degradation in both separation and detection tasks of overlapping sound events. In this paper, we propose a new method that utilizes K-Singular Value Decomposition (K-SVD) based dictionary to address and mitigate the part-based representation issue during the dictionary learning step. Subsequently, we calculate the gain using NMF in sound event detection step. We evaluate and confirm that overlapping sound event detection performance of the proposed method is better than the conventional method that utilizes NMF based dictionary.

• Journal of KIISE:Software and Applications
• /
• v.31 no.10
• /
• pp.1412-1417
• /
• 2004

Sparse coding or independent component analysis (ICA) which is a holistic representation, was successfully applied to elucidate early auditor${\gamma}$ processing and to the task of sound classification. In contrast, parts-based representation is an alternative way o) understanding object recognition in brain. In this thesis we employ the non-negative matrix factorization (NMF) which learns parts-based representation in the task of sound classification. Methods of feature extraction from the spectro-temporal sounds using the NMF in the absence or presence of noise, are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

• ETRI Journal
• /
• v.32 no.2
• /
• pp.345-347
• /
• 2010

In this letter, we present a new speech hash function based on the non-negative matrix factorization (NMF) of linear prediction coefficients (LPCs). First, linear prediction analysis is applied to the speech to obtain its LPCs, which represent the frequency shaping attributes of the vocal tract. Then, the NMF is performed on the LPCs to capture the speech's local feature, which is then used for hash vector generation. Experimental results demonstrate the effectiveness of the proposed hash function in terms of discrimination and robustness against various types of content preserving signal processing manipulations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2017.11a
• /
• pp.202-203
• /
• 2017

비음수 행렬 인수분해(Non-negative Matrix Factorization, NMF)의 신호분리 성능을 개선하기 위해 희소조건을 인가한 방법이 희소 비음수 행렬 인수분해 알고리즘(Sparse NMF, SNMF)이다. 기존의 SNMF 알고리즘은 개별 음원의 희소성을 고려하지 않고 임의로 결정한 희소 조건을 사용한다. 본 논문에서는 음원의 특성에 따른 희소성을 추정하고 이를 SNMF 학습알고리즘에 적용하는 새로운 신호분리 기법을 제안한다. 혼합 신호에서의 잡음제거 실험을 통해, 제안한 방법이 기존의 NMF와 SNMF에 비해 성능이 더 우수함을 보였다.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.4
• /
• pp.11-18
• /
• 2008

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the re-weighted term based NMF(non-negative matrix factorization) to cluster documents relevant to a user's requirement. The proposed model uses the re-weighted term by using user feedback to reduce the gap between the user's requirement for document classification and the document clusters by means of machine. The Proposed method can improve the quality of document clustering because the re-weighted terms. the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.2
• /
• pp.65-76
• /
• 2004

Today, most documents are electronically produced and their paleography is digitalized by imaging, resulting in a tremendous number of electronic documents in the shape of images. Therefore, to process these document images, many methods of document structure analysis and recognition have already been proposed, including font classification. Accordingly, the current paper proposes a font classification method for document images that uses non-negative matrix factorization (NMF), which is able to learn part-based representations of objects. In the proposed method, spatially total features of font images are automatically extracted using NMF, then the appropriateness of the features specifying each font is investigated. The proposed method is expected to improve the performance of optical character recognition (OCR), document indexing, and retrieval systems, when such systems adopt a font classifier as a preprocessor.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.6
• /
• pp.682-689
• /
• 2019

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.1
• /
• pp.32-41
• /
• 2023

Recently, studies on reverberation suppression using Non-negative Matrix Factorization (NMF) have been actively conducted. The NMF method uses a cost function based on the Kullback-Leibler divergence for optimization. And some constraints are added such as temporal continuity, pulse length, and energy ratio between reverberation and target. The tendency of constraints are controlled by hyperparameters. Therefore, in order to effectively suppress reverberation, hyperparameters need to be optimized. However, related studies are insufficient so far. In this paper, the reverberation suppression performance according to the three hyperparameters of the NMF was analyzed by using sea experimental data. As a result of analysis, when the value of hyperparameters for time continuity and pulse length were high, the energy ratio between the reverberation and the target showed better performance at less than 0.4, but it was confirmed that there was variability depending on the ocean environment. It is expected that the analysis results in this paper will be utilized as a useful guideline for planning precise experiments for optimizing hyperparameters of NMF in the future.

• Journal of applied mathematics & informatics
• /
• v.30 no.3_4
• /
• pp.365-380
• /
• 2012

Non-negative matrix factorization (NMF) is a very efficient method to explain the relationship between functions for finding basis information of multivariate nonnegative data. The multiplicative update (MU) algorithm is a popular approach to solve the NMF problem, but it fails to approach a stationary point and has inner iteration and zero divisor. So the elementwisely alternating projected gradient (eAPG) algorithm was proposed to overcome the defects. In this paper, we use the fact that the equilibrium point is stable to prove the convergence of the eAPG algorithm. By using a classic model, the equilibrium point is obtained and the invariant sets are constructed to guarantee the integrity of the stability. Finally, the convergence conditions of the eAPG algorithm are obtained, which can accelerate the convergence. In addition, the conditions, which satisfy that the non-zero equilibrium point exists and is stable, can cause that the algorithm converges to different values. Both of them are confirmed in the experiments. And we give the mathematical proof that the eAPG algorithm can reach the appointed precision at the least iterations compared to the MU algorithm. Thus, we theoretically illustrate the advantages of the eAPG algorithm.