Commute time guided transformation for feature extraction

Deng, Yue; Dai, Qionghai; Wang, Ruiping; Zhang, Zengke

doi:10.1016/j.cviu.2011.11.002

Cited by 19 publications

(10 citation statements)

References 25 publications

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“…측정하거나 분류하는 것은 매우 어려운 일이다. 이를 해결하기 위하여 본 논문에서는 CTG(commute time guided) 변환 [5] 을 이용하여 다양체 임베딩이 인식 분야 에도 적용 가능함을 보인다. 우선, 패치 노드…”

Section: ⅲ 패치 그래프 구성unclassified

A Study on Classification of Waveforms Using Manifold Embedding Based on Commute Time

Hahn¹

2014

Journal of the Institute of Electronics and Information Enginee

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In this paper a commute time embedding is implemented by organizing patches according to the graph-based metric, and its properties are investigated via changing the number of nodes on the graph.. It is shown that manifold embedding methods generate the intrinsic geometric structures when waveforms such as speech or music instrumental sound signals are embedded on the low dimensional Euclidean space. Basically manifold embedding algorithms only project the training samples on the graph into an embedding subspace but can not generalize the learning results to test samples. They are very effective for data clustering but are not appropriate for classification or recognition. In this paper a commute time guided transform is adopted to enhance the generalization ability and its performance is analyzed by applying it to the classification of 6 kinds of music instrumental sounds.

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Section: ⅲ 패치 그래프 구성unclassified

A Study on Classification of Waveforms Using Manifold Embedding Based on Commute Time

Hahn¹

2014

Journal of the Institute of Electronics and Information Enginee

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“…In the embedded space, the original graph metric is well preserved by the Euclidean distance. According to [8], we give the graph metric guided transformation,…”

Section: Describing Feature Similarity Via Graphmentioning

confidence: 99%

“…However, the computational cost is extremely huge for real time usage. Fortunately, inspired by [8], we propose to embed the graph into an Euclidean space with a linear projection matrix. In the embedded space, the original graph metric is well preserved by the Euclidean distance.…”

Section: Describing Feature Similarity Via Graphmentioning

confidence: 99%

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Visual words assignment on a graph via minimal mutual information loss

Qian¹,

Deng²,

Dai³

et al. 2012

Procedings of the British Machine Vision Conference 2012

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Visual codewords assignment plays an important role in many Bag of Features (BoF) models for image understanding and visual recognition. It allocates image descriptors to the most similar codewords in the pre-configured visual dictionary to generate descriptive histogram for the consequent categorization. Nevertheless, existing assignment approaches, e.g. nearest neighbors strategy and Gaussian similarity, suffer from two problems:1) too strong Euclidean assumption and 2) neglecting the label information of the local features. Accordingly, in this paper, we propose an assignment method to simultaneously consider the above two issues in a unified model via graph learning and information theoretic criterions. For learning, the proposed model can be efficiently solved in a closed-form with the reasonable graph topology invariant approximation. Moreover, the learned projections enable us to extend the assignment ability to the out-of-sample visual features beyond the initial training graph. Experiments on our own manifold dataset and two benchmarks verify the effectiveness of the proposed graph assignment method.

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“…Learning the intrinsic data structures via matrix analysis [1][2] has received wide attention in many fields, e.g., neural network [3], learning system [4] [5], control theory [6], computer vision [7][8] and pattern recognition [9] [10]. There are quite a number of efficient mathematical tools for rank analysis, e.g., Principal Component Analysis (PCA) and Singular Value Decomposition (SVD).…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Structure Learning via Nonconvex Heuristic Recovery

Deng

Dai

Liu

et al. 2013

IEEE Trans. Neural Netw. Learning Syst.

106

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Abstract-Recovering intrinsic data structure from corrupted observations plays an important role in various tasks in the communities of machine learning and signal processing. In this paper, we propose a novel model, named log-sum heuristic recovery (LHR), to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilize ℓ1 norm to measure the sparseness, LHR introduces a more reasonable log-sum measurement to enhance the sparsity in both the intrinsic low-rank structure and in the sparse corruptions. Although the proposed LHR optimization is no longer convex, it still can be effectively solved by a majorizationminimization (MM) type algorithm, with which the non-convex objective function is iteratively replaced by its convex surrogate and LHR finally falls into the general framework of reweighed approaches. We prove that the MM-type algorithm can converge to a stationary point after successive iteration. We test the performance of our proposed model by applying it to solve two typical problems: robust principal component analysis (RPCA) and low-rank representation (LRR). For RPCA, we compare LHR with the benchmark Principal Component Pursuit (PCP) method from both the perspectives of simulations and practical applications. For LRR, we apply LHR to compute the lowrank representation matrix for motion segmentation and stock clustering. Experimental results on low rank structure learning demonstrate that the proposed Log-sum based model performs much better than the ℓ1-based method on for data with higher rank and with denser corruptions.Index Terms-Log-sum heuristic, compressive sensing, sparse optimization, matrix learning, nuclear norm minimization.

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Commute time guided transformation for feature extraction

Cited by 19 publications

References 25 publications

A Study on Classification of Waveforms Using Manifold Embedding Based on Commute Time

A Study on Classification of Waveforms Using Manifold Embedding Based on Commute Time

Visual words assignment on a graph via minimal mutual information loss

Low-Rank Structure Learning via Nonconvex Heuristic Recovery

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