  In the advent of big data era, interactive visualization of large data sets
consisting 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-art
data embedding (DE) methods of N-D data into 2-D (3-D) visually perceptible
space (e.g., based on t-SNE concept) are too demanding computationally to be
efficiently employed for interactive data analytics of large and
high-dimensional datasets. Herein we present a simple method, ivhd (interactive
visualization of high-dimensional data tool), which radically outperforms the
modern data-embedding algorithms in both computational and memory loads, while
retaining high quality of N-D data embedding in 2-D (3-D). We show that DE
problem is equivalent to the nearest neighbor nn-graph visualization, where
only 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 other
samples -- is defined. These improvements reduce the time-complexity and memory
load from O(M log M) to O(M), and ensure minimal O(M) proportionality
coefficient as well. We demonstrate high efficiency, quality and robustness of
ivhd on popular benchmark datasets such as MNIST, 20NG, NORB and RCV1.
