We evaluated these modalities for exploring SDDD in VR, in an experiment with 18 participants. In this paper, we evaluated two interaction modalities for Spatial Dense Dynamic Data (SDDD), adapted from existing interaction methods for static and spatial data. However, tracking dynamically moving data in a dense 3D environment poses the challenge of ever-changing occlusion, especially if motion carries relevant information, which is lost in still images. Research has proposed various interaction techniques to manage the occlusion of 3D data in Virtual Reality (VR), e.g., via gradual refinement. Comprehensive experiments using real-world data demonstrate the effectiveness of SABDR in different subspace clustering applications. In this way, the sparsity parameter selection problem is re-formulated as a robust approximation of target between-clusters distances. Specifically, we derive the Euclidean distance between the embeddings of different clusters to develop a computationally efficient density-based clustering algorithm. We propose a new sparsity-aware block diagonal representation (SABDR) method that robustly estimates the appropriate sparsity level by leveraging upon the geometrical analysis of the low-dimensional structure in spectral clustering. Structuring a block diagonal matrix can be challenging due to the determination of an appropriate sparsity level, especially when outliers and heavy-tailed noise obscure the underlying subspaces. A block diagonally structured affinity matrix is an informative prior for subspace clustering which embeds the data points in a union of low-dimensional subspaces.
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