diff --git a/src/aspire/numeric/complex_pca/complex_pca.py b/src/aspire/numeric/complex_pca/complex_pca.py
index 5c8cd1e2d6..bd270ef64b 100644
--- a/src/aspire/numeric/complex_pca/complex_pca.py
+++ b/src/aspire/numeric/complex_pca/complex_pca.py
@@ -13,7 +13,10 @@
 
 import numpy as np
 import scipy.sparse as sp
+import sklearn
+from packaging.version import Version
 from sklearn.decomposition import PCA
+from sklearn.utils._array_api import get_namespace
 
 from .validation import check_array
 
@@ -45,6 +48,8 @@ def _fit(self, X):
             allow_complex=True,
         )
 
+        xp, is_array_api_compliant = get_namespace(X)
+
         # Handle n_components==None
         if self.n_components is None:
             if self.svd_solver != "arpack":
@@ -66,11 +71,22 @@ def _fit(self, X):
             else:
                 self._fit_svd_solver = "full"
 
+        # sci-kit changed `_fit_*()` API in latest release v1.5.0
+        # which supports Python 3.9 - 3.12. This can be removed after
+        # our minimal support is Python 3.9.
+        API_dep = Version(sklearn.__version__) < Version("1.5.0")
+
         # Call different fits for either full or truncated SVD
         if self._fit_svd_solver == "full":
-            return self._fit_full(X, n_components)
+            if API_dep:
+                return self._fit_full(X, n_components)
+            else:
+                return self._fit_full(X, n_components, xp, is_array_api_compliant)
         elif self._fit_svd_solver in ["arpack", "randomized"]:
-            return self._fit_truncated(X, n_components, self._fit_svd_solver)
+            if API_dep:
+                return self._fit_truncated(X, n_components, self._fit_svd_solver)
+            else:
+                return self._fit_truncated(X, n_components, xp)
         else:
             raise ValueError(
                 "Unrecognized svd_solver='{0}'" "".format(self._fit_svd_solver)