CI-SNF: Exploiting contextual information to improve SNF based information retrieval

Chen, Ning

doi:10.1016/j.inffus.2018.08.004

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…Similarity network fusion (SNF) is a joint random walk technique that was devised to leverage the strengths of different hand-designed similarity measures for shape classification 2D contours in images [1]. It has since been used in such tasks as cancer phenotype discrimination [2], image retrieval [3], and drug taxonomy [3]. SNF was introduced to the music information retrieval community by the authors of [4] to leverage different cross-similarity alignment scores in automatic cover song identification.…”

Section: Related Workmentioning

confidence: 99%

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Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion

Tralie

McFee

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We describe a novel pipeline to automatically discover hierarchies of repeated sections in musical audio. The proposed method uses similarity network fusion (SNF) to combine different frame-level features into clean affinity matrices, which are then used as input to spectral clustering. While prior spectral clustering approaches to music structure analysis have pre-processed affinity matrices with heuristics specifically designed for this task, we show that the SNF approach directly yields segmentations which agree better with human annotators, as measured by the "L-measure" metric for hierarchical annotations. Furthermore, the SNF approach immediately supports arbitrarily many input features, allowing us to simultaneously discover structure encoded in timbral, harmonic, and rhythmic representations without any changes to the base algorithm.

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Section: Related Workmentioning

confidence: 99%

“…where κ the number of nearest neighbors which is fixed a priori (we will explore the effect of κ in Section 3. 3), and N f κ (i) are the indices of the κ nearest neighbors of i, as measured by D f . SNF then defines two additional normalized versions P f and S f of each affinity matrix as follows…”

Section: Fusionmentioning

confidence: 99%

Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion

Tralie

McFee

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Upstream fusion of SSMs arising from two or more modalities can then be performed (Section 5) using similarity network fusion (SNF, [25], [26]), a random-walk-based technique which is designed to create an SSM which combines the strengths of the individual matrices. SNF has been applied to different pre-processed modalities arising from musical audio [21] and to improving object level comparisons between 2D shapes [25], cancer phenotypes [26], [8], and image collections [8], but to our knowledge, this is the first paper that does so across audio and video modalities for individual objects. It is also possible (Section 7) to apply SNF to SSMs defined on the object level rather than at the feature level, leading to a downstream fusion technique.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion

Tralie

Bendich

Harer

2019

2019 IEEE Aerospace Conference

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In this work, we address fusion of heterogeneous sensor data using wavelet-based summaries of fused self-similarity information from each sensor. The technique we develop is quite general, does not require domain specific knowledge or physical models, and requires no training. Nonetheless, it can perform surprisingly well at the general task of differentiating classes of time-ordered behavior sequences which are sensed by more than one modality. As a demonstration of our capabilities in the audio to video context, we focus on the differentiation of speech sequences.Data from two or more modalities first are represented using self-similarity matrices(SSMs) corresponding to time-ordered point clouds in feature spaces of each of these data sources; we note that these feature spaces can be of entirely different scale and dimensionality.A fused similarity template is then derived from the modalityspecific SSMs using a technique called similarity network fusion (SNF). We investigate pipelines using SNF as both an upstream (feature-level) and a downstream (ranking-level) fusion technique. Multiscale geometric features of this template are then extracted using a recently-developed technique called the scattering transform, and these features are then used to differentiate speech sequences. This method outperforms unsupervised techniques which operate directly on the raw data, and it also outperforms stovepiped methods which operate on SSMs separately derived from the distinct modalities. The benefits of this method become even more apparent as the simulated peak signal to noise ratio decreases.

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“…In regard to research into information retrieval, Chen [20] noted that similarity networks include important topological characteristics and models which are of great importance to understanding the interaction between samples in large datasets. To set up an interactive, comprehensive view in a dataset, he proposed to integrate the Similarity Network Fusion (SNF) technology into a full spectrum of similarity networks based on them having different data types.…”

Section: Introductionmentioning

confidence: 99%

Mobile Phone Recommender System Using Information Retrieval Technology by Integrating Fuzzy OWA and Gray Relational Analysis

Wang

2018

Information

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With the advancement and diversification of information retrieval technology, such technology has been widely applied in recent years in personalized information recommender systems (RSs) and e-commerce RSs in addition to data-mining applications, especially with respect to mobile phone purchases. By integrating the weights of fuzzy ordered weighted averaging (OWA) and gray relational analysis, this research calculated the recommended F1 indices of three weight calculation methods to be 20.5%, 14.36%, and 16.43% after an examination by 30 experimenters. According to the operational results attained by the 30 experimenters, the recommended products obtained by the fuzzy OWA and gray relational analysis calculation method covered the products recommended by the other two weight calculation methods with a higher recommendation effect.

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CI-SNF: Exploiting contextual information to improve SNF based information retrieval

Cited by 7 publications

References 42 publications

Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion

Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion

Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion

Mobile Phone Recommender System Using Information Retrieval Technology by Integrating Fuzzy OWA and Gray Relational Analysis

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