Embedding-Based Representation of Signal Geometry

Dimensionality reduction of visual features for efficient retrieval and classification

“…However, as the set becomes denser, other measures of complexity, such as the set covering number, can be used to better characterize the embedding dimension. For examples, see [15][16][17] and references therein. Thus, embedding dimensionality can be kept low, even for very large, and increasing in size, datasets.…”

“…Specifically, D 0 is proportional to ; a larger choice of implies that a larger range of distances is preserved. On the other hand, as described in [15,25], given a fixed rate, preserving a larger range of distances by selecting a larger reduces the fidelity with which these distances are preserved.…”

“…The exact details, including the matching algorithm and extensive results, have been described in recent papers [15,18,25]. Here, we summarize the experiments that examine the performance of our approach and the trade-off between number of measurements and number of bits per measurement with respect to that performance.…”