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Our method involves two main computation steps. First, it selects an appropriate neighbors set for each data points such that all neighbors in a neighbors set ...
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Dec 7, 2023 · This approach stems from the notion that, given a low-dimensional manifold in a high-dimensional space, the local space around each data point ...
Dec 7, 2023 · In this paper, we propose an adaptive version of ISOMAP that integrates the advantages of local and global manifold learning algorithms. Faster ...
Abstract. Popular nonlinear dimensionality reduction algorithms, e.g., SIE and. Isomap suffer a difficulty in common: global neighborhood parameters often.
In this paper, we propose a local nonlinear dimensionality reduction method named Vec2vec, which employs a neural network with only one hidden layer to reduce ...
The most promising solutions involve performing dimensionality reduction on the data, then indexing the reduced data with a multidimensional index structure.
Abstract. Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because of the typically ...
ABSTRACT. In this paper we address the issue of using local embeddings for data visualization in two and three dimensions, and for classification.
Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighbor- hood-preserving embeddings of ...
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We propose a new variant of Isomap algorithm based on local linear properties of manifolds to increase its robustness to short-circuiting. We demonstrate that ...