Novel Indexing Strategy and Similarity Measures for Gaussian Mixture Models
| Autor: | Wei Ye, Christian Bohm, Linfei Zhou, Claudia Plant, Bianca Wackersreuther |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Normalization (statistics)
business.industry Computer science Nearest neighbor search Gaussian Search engine indexing 020206 networking & telecommunications Pattern recognition 02 engineering and technology Speaker recognition Mixture model computer.software_genre symbols.namesake Transformation (function) Similarity (network science) 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Data mining Artificial intelligence Cluster analysis business computer Image retrieval |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319644707 DEXA (2) |
| DOI: | 10.1007/978-3-319-64471-4_14 |
| Popis: | Efficient similarity search for data with complex structures is a challenging task in many modern data mining applications, such as image retrieval, speaker recognition and stock market analysis. A common way to model these data objects is using Gaussian Mixture Models which has the ability to approximate arbitrary distributions in a concise way. To facilitate efficient queries, indexes are essential techniques. However, due different numbers of components in Gaussian Mixture Models, existing index methods tend to break down in performance. In this paper we propose a novel technique Normalized Transformation that reorganizes the index structure to account for different numbers of components in Gaussian Mixture Models. In addition, Normalized Transformation enables us to derive a set of similarity measures on the basis of existing ones that have close-form expression. Extensive experiments demonstrate the effectiveness of proposed technique for Gaussian component-based indexing and the performance of the novel similarity measures for clustering and classification. |
| Databáze: | OpenAIRE |
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