Subspace Clustering via Tangent Cones

Authors: Amin Jalali, Rebecca Willett

NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In this section, we illustrate the performance of the CSC method on some small examples. First, we examine the role of the parameter β in CSC1(β, x, x ) and its effect on the false positive and true positive rates in practice. In the first experiment, we have k = 5 subspaces, each with dimension d = 5, in an n = 10 dimensional space, and we draw 30 samples from each of the k 5-dimensional subspaces. We then run CSC1(β, x, x ) for a variety of values of β between one and six over 15 random trials; In Figures 2(b), 2(c), and 2(d), we show the results of each trial in thin lines and the means across trials in thick lines (Figure 2(c) shows the median).
Researcher Affiliation Academia Amin Jalali Wisconsin Institute for Discovery University of Wisconsin Madison, WI 53715 amin.jalali@wisc.edu Rebecca Willett Department of Electrical and Computer Engineering University of Wisconsin Madison, WI 53706 willett@discovery.wisc.edu
Pseudocode No The paper describes steps and formulations but does not present them in a clearly labeled 'Pseudocode' or 'Algorithm' block.
Open Source Code No The paper does not provide any concrete access information to source code (e.g., specific repository link, explicit code release statement, or code in supplementary materials).
Open Datasets No The paper describes synthetic data generation (
Dataset Splits No The paper describes synthetic data generation and experiments (e.g.,
Hardware Specification No The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It only describes the experimental setup in terms of number of subspaces, dimensions, and samples.
Software Dependencies No The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment.
Experiment Setup Yes In the first experiment, we have k = 5 subspaces, each with dimension d = 5, in an n = 10 dimensional space, and we draw 30 samples from each of the k 5-dimensional subspaces. We then run CSC1(β, x, x ) for a variety of values of β between one and six over 15 random trials.