pith:NUSKXQ2V
Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment
Hierarchical imputation of missing cluster assignments via cross-view similarity followed by feature reconstruction from intra-cluster statistics allows accurate shared clustering from partially observed multi-view data.
arxiv:2601.09051 v1 · 2026-01-14 · cs.LG
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\usepackage{pith}
\pithnumber{NUSKXQ2VJL7WI63CDKUESJWFPH}
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Record completeness
Claims
Experiments on benchmarks demonstrate that our framework achieves superior performance under varying levels of missingness.
That estimating missing cluster assignments from cross-view contrastive similarity and reconstructing features from intra-cluster statistics introduces no systematic bias and preserves semantic consistency.
DIMVC-HIA is a deep method for clustering multi-view data with missing views by hierarchically imputing assignments and features then aligning clusters for consistency and compactness.
Formal links
Receipt and verification
| First computed | 2026-05-18T03:09:32.113236Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6d24abc3554aff647b621aa84926c579e26f3906558371ca4583330e1c713408
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/NUSKXQ2VJL7WI63CDKUESJWFPH \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 6d24abc3554aff647b621aa84926c579e26f3906558371ca4583330e1c713408
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2026-01-14T00:46:00Z",
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