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In the advent of big data era, interactive visualization of large data setsconsisting of M*10^5+ high-dimensional feature vectors of length N (N ~ 10^3+),is an indispensable tool for data exploratory analysis. The state-of-the-artdata embedding (DE) methods of N-D data into 2-D (3-D) visually perceptiblespace (e.g., based on t-SNE concept) are too demanding computationally to beefficiently employed for interactive data analytics of large andhigh-dimensional datasets. Herein we present a simple method, ivhd (interactivevisualization of high-dimensional data tool), which radically outperforms themodern data-embedding algorithms in both computational and memory loads, whileretaining high quality of N-D data embedding in 2-D (3-D). We show that DEproblem is equivalent to the nearest neighbor nn-graph visualization, whereonly indices of a few nearest neighbors of each data sample has to be known,and binary distance between data samples -- 0 to the nearest and 1 to the othersamples -- is defined. These improvements reduce the time-complexity and memoryload from O(M log M) to O(M), and ensure minimal O(M) proportionalitycoefficient as well. We demonstrate high efficiency, quality and robustness ofivhd on popular benchmark datasets such as MNIST, 20NG, NORB and RCV1.

2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements