Author here.
I built this because I needed to run dimensionality reduction entirely in the browser (client-side) for an interactive tool. The standard options (UMAP, t-SNE) were either too heavy for JS/WASM or required a GPU backend to run at acceptable speeds for interactive use.
This approach ("Sine Landmark Reduction") uses linearised trilateration—similar to GPS positioning—against a synthetic "sine skeleton" of landmarks.
The main trade-offs:
It is O(N) and deterministic (solves Ax=b instead of iterative gradient descent).
It forces the topology onto a loop structure, so it is less accurate than UMAP for complex manifolds (like Swiss Rolls), but it guarantees a clean layout for user interfaces.
It can project ~9k points (50 dims) to 3D in about 2 seconds on a laptop CPU. Python implementation and math details are in the post. Happy to answer questions!
Fwiw, we are heavy UMAP users (pygraphistry), and find UMAP CPU fine for interactive use at up to 30K rows and GPU at 100K rows, then generally switch to a trained mode when > 100K rows. Our use case is often highly visual - see correlations, and link together similar entities into explorable & interactive network diagrams. For headless, like in daily anomaly detection, we will do this to much larger scales.
We see a lot of wide social, log, and cyber data where this works, anywhere from 5-200 dim. Our bio users are trickier, as we can have 1K+ dimensions pretty fast. We find success there too, and mostly get into preconditioning tricks for those.
At the same time, I'm increasingly thinking of learning neural embeddings in general for these instead of traditional clustering algorithms. As scales go up, the performance argument here goes up too.
I was not aware this existed and it looks cool! I am definitely going to take out some time to explore it further.
I have a couple of questions for now:
(1) I am confused by your last sentence. It seems you're saying embeddings are a substitute for clustering. My understanding is that you usually apply a clustering algorithm over embeddings - good embeddings just ensure that the grouping produced by the clustering algo "makes sense".
(2) Have you tried PaCMAP? I found it to produce high quality and quick results when I tried it. Haven't tried it in a while though - and I vaguely remember that it won't install properly on my machine (a Mac) the last time I had reached out for it. Their group has some new stuff coming out too (on the linked page).
We generally run UMAP on regular semi-structured data like database query results. We automatically feature encode that for dates, bools, low-cardinality vals, etc. If there is text, and the right libs available, we may also use text embeddings for those columns. (cucat is our GPU port of dirtycat/skrub, and pygraphistry's .featurize() wraps around that).
My last sentence was on more valuable problems, we are finding it makes sense to go straight to GNNs, LLMs, etc and embed multidimensional data that way vs via UMAP dim reductions. We can still use UMAP as a generic hammer to control further dimensionality reductions, but the 'hard' part would be handled by the model. With neural graph layouts, we can potentially even skip the UMAP for that too.
Re:pacmap, we have been eyeing several new tools here, but so far haven't felt the need internally to go from UMAP to them. We'd need to see significant improvements given the quality engineering in UMAP has set the bar high. In theory I can imagine some tools doing better in the future, but the creators have't done the engineering investment, so internally, we rather stay with UMAP. We make our API pluggable, so you can pass in results from other tools, and we haven't heard much from that path from others.
Without looking at the code, O(N * k) with N = 9000 points and k = 50 dimensions should take in the order of milliseconds, not seconds. Did you profile your code to see whether there is perhaps something that takes an unexpected amount of time?
The '2 seconds' figure comes from the end-to-end time on a standard laptop. I quoted 2s to set realistic expectations for the user experience, not the CPU cycle count. You are right that the core linear algebra (Ax=b) is milliseconds. The bottleneck is the DOM/rendering overhead, but strictly speaking, the math itself is blazing fast.
Each of the N data points is processed through several expensive linear algebra operations. O(N * k) just expresses that if you double N, the runtime also at most doubles. It doesn't mean it has to be fast in an absolute sense for any particular value of N and k.
FWIW, there are iterative SVD implementations out there that could potentially be useful as well in certain contexts when you get more data over time in a streamed manner.
Thanks! You nailed the intuition! Yes, it shares DNA with Landmark MDS, but we needed something strictly deterministic for the UI.
Re: Publishing: We don't have a paper planned for this specific visualisation technique yet. I just wanted to open-source it because it solved a major bottleneck for our dashboard.
However, our main research focus at Thingbook is DriftMind (a cold start streaming forecaster and anomaly detector, preprint here: https://www.researchgate.net/publication/398142288_DriftMind...). That paper is currently under peer review! It shares the same 'efficiency-first' philosophy as this visualisation tool
The claim of linear runtime is only true if K is independent of the dataset size, so it would have been nice to see an exploration of how different values of K impact results. I.e. does clustering get better for larger K, if so how much? The values 50 and 100 seem arbitrary and even suspiciously close to sqrt(N) for the 9K dataset.
To clarify: K is a fixed hyperparameter in this implementation, strictly independent of N. Whether we process 9k points or 90k points, we keep K at ~100.
We found that increasing K yields diminishing returns very quickly. Since the landmarks are generated along a fixed synthetic topology, increasing K essentially just increases resolution along that specific curve, but once you have enough landmarks to define the curve's structure, adding more doesn't reveal new topology… it just adds computational cost to the distance matrix calculation.
Re: sqrt(N): That is purely a coincidence!
Something seems off here. t-SNE should not be taking 15-25 seconds for only 5k points and 20 dimensions, but rather somewhere like 1-2 seconds. Also since the given alternative is not as good, you would probably be able to reduce the iterations somewhat with t-SNE if speed is wanted at the risk of quality. Alternatively UMAP for this would be milliseconds, bordering on real-time with aggressive tuning.
Thanks for your comment! You are right, Barnes-Hut implementation brings UMAP down to O(N log N). I should have been more precise in the document. The main point is that even O(N log N) could be too much if you run this in a browser.. Thanks for clarifying!
Thanks for your comment. You are spot on, that is effectively the standard Nyström/Landmark MDS approach.
The technique actually supports both modes in the implementation (synthetic skeleton or random subsampling). However, for this browser visualisation, we default to the synthetic sine skeleton for two reasons:
1. Determinism: Random landmarks produce a different layout every time you calculate the projection. For a user interface, we needed the layout to be identical every time the user loads the data, without needing to cache a random seed.
2. Topology Forcing: By using a fixed sine/loop skeleton, we implicitly 'unroll' the high-dimensional data onto a clean reduced structure. We found this easier for users to visually navigate compared to the unpredictable geometry that comes from a random subset
You don't need a "proper" random selection: if your points are sorted deterministically and not too adversarially, any reasonably unbiased selection (e.g. every Nth point) is pseudorandom.
```
import numpy as np
import time
import matplotlib.pyplot as plt
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.datasets import load_digits
from sklearn.metrics import pairwise_distances
from sklearn.manifold import TSNE
try:
import umap
HAS_UMAP = True
except ImportError:
HAS_UMAP = False
print("Warning: 'umap-learn' not installed. Comparison will be skipped.")
This approach ("Sine Landmark Reduction") uses linearised trilateration—similar to GPS positioning—against a synthetic "sine skeleton" of landmarks.
The main trade-offs:
It is O(N) and deterministic (solves Ax=b instead of iterative gradient descent).
It forces the topology onto a loop structure, so it is less accurate than UMAP for complex manifolds (like Swiss Rolls), but it guarantees a clean layout for user interfaces.
It can project ~9k points (50 dims) to 3D in about 2 seconds on a laptop CPU. Python implementation and math details are in the post. Happy to answer questions!
We see a lot of wide social, log, and cyber data where this works, anywhere from 5-200 dim. Our bio users are trickier, as we can have 1K+ dimensions pretty fast. We find success there too, and mostly get into preconditioning tricks for those.
At the same time, I'm increasingly thinking of learning neural embeddings in general for these instead of traditional clustering algorithms. As scales go up, the performance argument here goes up too.
I have a couple of questions for now: (1) I am confused by your last sentence. It seems you're saying embeddings are a substitute for clustering. My understanding is that you usually apply a clustering algorithm over embeddings - good embeddings just ensure that the grouping produced by the clustering algo "makes sense".
(2) Have you tried PaCMAP? I found it to produce high quality and quick results when I tried it. Haven't tried it in a while though - and I vaguely remember that it won't install properly on my machine (a Mac) the last time I had reached out for it. Their group has some new stuff coming out too (on the linked page).
[1] https://github.com/YingfanWang/PaCMAP
My last sentence was on more valuable problems, we are finding it makes sense to go straight to GNNs, LLMs, etc and embed multidimensional data that way vs via UMAP dim reductions. We can still use UMAP as a generic hammer to control further dimensionality reductions, but the 'hard' part would be handled by the model. With neural graph layouts, we can potentially even skip the UMAP for that too.
Re:pacmap, we have been eyeing several new tools here, but so far haven't felt the need internally to go from UMAP to them. We'd need to see significant improvements given the quality engineering in UMAP has set the bar high. In theory I can imagine some tools doing better in the future, but the creators have't done the engineering investment, so internally, we rather stay with UMAP. We make our API pluggable, so you can pass in results from other tools, and we haven't heard much from that path from others.
To clarify: K is a fixed hyperparameter in this implementation, strictly independent of N. Whether we process 9k points or 90k points, we keep K at ~100. We found that increasing K yields diminishing returns very quickly. Since the landmarks are generated along a fixed synthetic topology, increasing K essentially just increases resolution along that specific curve, but once you have enough landmarks to define the curve's structure, adding more doesn't reveal new topology… it just adds computational cost to the distance matrix calculation. Re: sqrt(N): That is purely a coincidence!
UMAP is not O(n^2) it is O(n log n).
The technique actually supports both modes in the implementation (synthetic skeleton or random subsampling). However, for this browser visualisation, we default to the synthetic sine skeleton for two reasons:
1. Determinism: Random landmarks produce a different layout every time you calculate the projection. For a user interface, we needed the layout to be identical every time the user loads the data, without needing to cache a random seed. 2. Topology Forcing: By using a fixed sine/loop skeleton, we implicitly 'unroll' the high-dimensional data onto a clean reduced structure. We found this easier for users to visually navigate compared to the unpredictable geometry that comes from a random subset
``` import numpy as np import time import matplotlib.pyplot as plt from sklearn.base import BaseEstimator, TransformerMixin from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler, MinMaxScaler from sklearn.datasets import load_digits from sklearn.metrics import pairwise_distances from sklearn.manifold import TSNE
try: import umap HAS_UMAP = True except ImportError: HAS_UMAP = False print("Warning: 'umap-learn' not installed. Comparison will be skipped.")
class SineLandmarkReduction(BaseEstimator, TransformerMixin): def __init__(self, n_components=2, n_landmarks=50, mode='data_derived', # 'sine' or 'data_derived' distance_warping=1.0, random_state=42): self.n_components = n_components self.n_landmarks = n_landmarks self.mode = mode self.p = distance_warping self.random_state = random_state self.rng = np.random.RandomState(random_state)
if HAS_UMAP: digits = load_digits() X = digits.data y = digits.target else: print("Please install umap-learn to run the comparison: pip install umap-learn") ```